Qualitative Judgments Research Articles

Evaluation of a combined bachelor's (Bach)/MD program at UC San Diego School of Medicine

Purpose A substantial number of medical schools offer combined baccalaureate (BA)/ MD programs and grant conditional acceptance to medical school at the time of admission to the affiliated undergraduate program. The purpose of these programs includes attracting academically talented students, reducing competitive pressures and encouraging honor students to become physicians. Although many combined BA/ MD programs exist, evidence on their impact is largely lacking. In this study, we analyzed the combined BA/ MD program at the University of California, San Diego, School of Medicine (UCSD SOM), the Medical Scholars program. Participants’ demographics were analyzed as well as their performance in USMLE 1 and USMLE Step 2 CK exams. Methods Since its implementation in 2003, 107 students have been admitted via the Medical Scholars program until 2016. The quantitative admission criteria include a SAT score of 1500 and above or 2250 and above (depending of the SAT scoring system used) or an ACT score of 34 and above. Also, a High School GPA of four and above is required among other qualitative judgements (e.g. extracurricular activities, volunteer experience). Academic performance and demographics of students from the Medical Scholars program (n=107) were compared with non-Medical Scholars (n=1552) for the matriculating class of 2003 until 2016. Results A total of 107 students entered UCSD SOM from 2003-2016 through the Medical Scholars program. The average number of Medical Scholars was eight students (S.D.=4.1) per matriculating class. This relates to an average of six percent in each matriculating class. For Medical Scholars and non-Medical Scholars, there was no significant difference between their USMLE Step 1 (232 (S.D.=20) compared to 230 (S.D.=20); p=0.6, unpaired t-test) and USMLE Step 2 CK performance (244 (S.D.=17) compared to 239 (S.D.=19), p=0.3, unpaired t-test). Demographics of students participating in the Medical Scholars program from 2006-2016 (n=107) revealed a significant racial/ ethnic difference between Medical Scholars and non-Medical Scholars (p<0.0001, chi-square test). Eighty-seven percent of the Medical Scholars were Asian (n=93) compared to 38% (n=589) of non-Medical Scholars. Eleven percent (n=12) of the Medical Scholars were white versus 44% of the non-Medical Scholars (n=681). Minorities underrepresented in Medicine compromised only 2% (n=2) of the Medical Scholars compared to 18% (n=282) of non-Medical Scholars. In addition, only two percent (n=2) of Medical Scholars self-identified as socioeconomically disadvantaged compared to 19% (n=311) of the non-Medical Scholars. Discussion The academic performance of students in the Medical Scholars program was equivalent to students admitted via the traditional pathway. Analyzing the demographics of Medical Scholars revealed an absence of URiM students and also a lack of students with socioeconomic disadvantage. As medical schools have made significant changes to diversify their applicants and to include more URiM students as well as students with socioeconomic disadvantage our study suggests that combined BA/MD programs may enhance socioeconomic and racial/ethnic disparities. The results have implications for medical schools seeking to implement or redefine their combined BA/MD program.

The development and validation of the Multidimensional Paternal Perinatal Scale (MPPS)

The limited availability of screening tools for assessing fathers' emotional well-being during the perinatal period warrants the development of a new measure, especially since the existing measurements did not specifically focus on fathers' perinatal experiences. These existing measurements focus on the assessment of clinical symptoms rather than precursors that may negatively impact on perinatal father's well-being. Based on the paternal perinatal conceptual framework, the Multidimensional Paternal Perinatal Scale (MPPS) was developed to identify precursors that contribute to the father's emotional well-being during the perinatal period. This paper examines three steps of measurement development. The first step comprised item development based on the literature and qualitative findings. The second step comprised conducting the qualitative and quantitative judgment analysis of the MPPS content validity. The third step assessed inter-rater reliability. Two versions of the MPPS were developed: the antenatal and postnatal versions. The antenatal version of the MPPS includes 75 items and targets 11 precursors of paternal perinatal emotional well-being, while the postnatal version includes 103 items and targets 13 precursors of poor emotional well-being. The validity analysis showed that both the antenatal and postnatal subscales met content validity requirements. The overall Content Validity Index (CVI) of the antenatal version was equal to .95 for clarity and .99 for relevance, while the postnatal version showed a CVI of .98 for clarity and .93 for relevance. The inter-rater reliability for each of the antenatal and postnatal versions of the MPPS showed an AC2 of .67. The exploratory factor analysis derived a five-factor solution for the ANT-MPPS, and eight-factor solution for POST-MPPS. Both versions of the MPPS showed acceptable internal consistency. Overall, the findings showed adequate indices for content validity and inter-rater reliability of the new Multidimensional Paternal Perinatal Scales. Additionally, the MPPS demonstrated an acceptable construct validity and internal consistency. Results demonstrate that both versions of the scale successfully characterised antecedents of emotional well-being and as such can be used to identify fathers at risk of poor emotional well-being in the antenatal and postnatal environments.

52‐3: Student Paper: A Quantitative Method for L0‐Leakage Sensitivity in TFT‐LCDs Based on JND Energy Ratio

In this paper, a quantitative evaluation method of L0 light leakage sensitivity based on JND energy ratio is established by studying the optical characterization and physical significance of L0 light leakage phenomenon. This method uses continuous quantitative indicators instead of human eye evaluation, which fully improves the accuracy and repeatability, and it can also predict the L0 light leakage level after the test module is assembled into the system set. An optical test system was built to simulate the influence of the system set. The liquid crystal module samples with different glass thickness and different sealant were tested. The test results were consistent with the qualitative judgment results of the human eye after the test module is assembled into tablet PC, and the similar level that the human eye can't be distinguished can be quantitatively distinguished. The repeatability error of the method was only 0.89%.

An applied soundscape approach for acoustic evaluation – compatibility with ISO 12913

Soundscape treats the acoustical climate depending on the complex interaction among multiple sound sources, properties of the environment, and society. Research published on soundscape since 1970s, display a great variety of aims, area selections, evaluation criteria, and therefore, methodologies. The ISO 12913 series on soundscape was first published in 2014, and since then, it has been developed to harmonize definitions, to regulate methods for data collection and reporting requirements as well as data analysis with their interpretations. This paper presents a research that aims to build up an applied soundscape approach developed for the evaluation, conservation, and rehabilitation of acoustical comfort in urban areas. The conceptual frame of this study is based on a methodology derived from field studies aiming to relate objective and subjective assessments of the urban environment. Besides, binaural sound recordings using soundwalk method, surveys including questionnaires, and semantic evaluations by the pairs of adjectives were obtained from the area. It is confirmed that the binaural recordings represent the actual sound environment by statistical analysis between subjective data obtained from on-site and the laboratory via recordings. The subjective data (pairs of adjectives) are correlated with the objective ones (sound quality metrics calculated through the recordings). Thus, the approach enables the qualitative judgments from the quantitative data by utilizing the listed equations for pairs of adjectives that can be described by the sound quality metrics. It is also documented that satisfaction from the sound environment is directly related to satisfaction from soundmarks. The methodology suggested by the study will provide significant benefits in the time-consuming field studies on urban acoustical comfort by ensuring rapid and systematic progress in related processes, which are obtaining, collecting, organizing, and archiving data. Thus, the need for on-site studies will be minimized. Furthermore, it is believed that the outcomes of the study will contribute to ISO 12913-2 and 3.

Approaching of Financial Ratio Analysis of Canara Bank and ICICI Bank

A ratio is defined as ‘the indicated quotient of two mathematical expressions’ and as ‘the relationship between two quantitative terms between figures which have a cause and effect relationship or which are connected with each other in some manner or the other. The sources of primary data are survey, observation and experimentation and secondary data collected through various websites. A noticeable point is that a ratio reflecting a quantitative relationship helps to perform a qualitative judgment; such is the nature of all financial ratios “Ratio can assist management in its basic functions of forecasting, planning coordination, control and communication”. It is helpful to know about the liquidity, solvency, capital structure and profitability of an organization. It is helpful tool to aid in applying judgment, otherwise complex situations.

Detecting fake news on Facebook: The role of emotional intelligence.

The proliferation of fake news on social media is now a matter of considerable public and governmental concern. In 2016, the UK EU referendum and the US Presidential election were both marked by social media misinformation campaigns, which have subsequently reduced trust in democratic processes. More recently, during the COVID-19 pandemic, the acceptance of fake news has been shown to pose a threat to public health. Research on how to combat the false acceptance of fake news is still in its infancy. However, recent studies have started to focus on the psychological factors which might make some individuals less likely to fall for fake news. Here, we adopt that approach to assess whether individuals who show high levels of ‘emotional intelligence’ (EQ) are less likely to fall for fake news items. That is, are individuals who are better able to disregard the emotionally charged content of such items, better equipped to assess the veracity of the information. Using a sample of UK participants, an established measure of EQ and a novel fake news detection task, we report a significant positive relationship between individual differences in emotional intelligence and fake news detection ability. We also report a similar effect for higher levels of educational attainment, and we report some exploratory qualitative fake news judgement data. Our findings are discussed in terms of their applicability to practical short term (i.e. current Facebook user data) and medium term (i.e. emotional intelligence training) interventions which could enhance fake news detection.

ERP software selection using intuitionistic fuzzy and interval grey number-based MACBETH method

PurposeEnterprise Resource Planning (ERP) software which is a knowledge-based design on the interconnective communication of business units and information share, ensures that business processes such as finance, production, purchasing, sales, logistics and human resources, are integrated and gathered under one roof. This integrated system allows the company to make fast and accurate decisions and increases its competitiveness. Therefore, for an enterprise, choosing the suitable ERP software is extremely important. The aim of this study is to present new research on the ERP software selection process by clarifying the uncertainties and find suitable software in a computational way.Design/methodology/approachERP selection problem design includes uncertainties on the expert opinions and the criteria values using intuitionistic fuzzy set theory and interval grey-numbers to MACBETH multi criteria decision making method. In this paper, a new interval grey MACBETH method approach is proposed, and the degree of greyness approach is used for clarifying the uncertainties. Using this new approach in which grey numbers are used, it is aimed to observe the changes in the importance of the alternatives. Moreover, the intuitionistic fuzzy set method is applied by considering the importance of expert opinions separately.FindingsThe proposed method is based on quantitative decision making derived from qualitative judgments. The results given under uncertain conditions are compared with the results obtained under crisp conditions of the same methods. With the qualitative levels of experts reflected in the decision process, it is clearly seen that ERP software selection problem area has more effective alternative decision solutions to the uncertain environment, and decision makers should not undervalue the unsteadiness of criteria during ERP software selection process.Originality/valueThis study contributes to the relevant literature by (1) utilizing the MACBETH method in the selection of the ERP software by optimization, and (2) validating the importance of expert opinions with uncertainties on a proper ERP software selection procedure. So, the findings of this study can help the decision-makers to evaluate the ERP selection in uncertain conditions.

ТАРИФНАЯ И ИНВЕСТИЦИОННАЯ ПОЛИТИКА ЭНЕРГЕТИЧЕСКИХ ПРЕДПРИЯ-ТИЙ В УСЛОВИЯХ НИЗКОУГЛЕРОДНОГО ТРЕНДА ЭКОНОМИКИ

The article reveals the issues of changing approaches to the implementation of tariff and investment policy by energy enterprises within the framework of strategic objectives and programtarget documents for decarbonization of the economy. The importance of these issues is confirmed by the global trends of increasing attention to the negative impact of organizational, economic and technological solutions on global warming and the destruction of the ozone layer. Using a deductive approach, the authors draw conclusions regarding the impact of the investment policy of energy enterprises on their competitiveness potential. The authors draw the conclusions about the methods suitable for energy enterprises based on the analysis of qualitative information and expert judgment. An element of scientific novelty is the substantiation of the algorithm for planning the investment component of measures to reduce the carbon footprint of energy enterprises, carried out by the authors.

Reviewing the Test Reviews: Quality Judgments and Reviewer Agreements in the Mental Measurements Yearbook

ABSTRACT Buros’ Mental Measurements Yearbook (MMY) has provided professional reviews of commercially published psychological and educational tests for over 80 years. It serves as a kind of conscience for the testing industry. For a random sample of 50 entries in the 19th MMY (a total of 100 separate reviews) this study determined the level of qualitative judgment rendered by reviewers and the consistency of those independent reviewers in rendering judgments. Judgments of quality distributed themselves almost uniformly from very good to very bad across the 100 reviews. Agreement among reviewers for a given test was positive but relatively weak. We explore implications of the results and suggest follow-up investigations.

Development of a fuzzy-TOPSIS multi-criteria decision-making model for material selection with the integration of safety, health and environment risk assessment

This study presents the development of a fuzzy-TOPSIS multi-criteria decision-making (MCDM) model for material selection with integrated safety, health, and environment risk assessment. As safety, health, and environment is very much linked to sustainable design and manufacturing, it is imperative that safety, health, and environment is taken into consideration in ensuring a comprehensive and holistic material selection process. This paper shows the possibility of fuzzy logic utilization in assessing safety, health, and environment risk and proposed a methodology based on fuzzy-TOPSIS MCDM model for material selection suitable for the manufacturing sector. This method has the capability of producing a ranking result with strong reasonings. The proposed method facilitates the designer to select, evaluate, and rank material alternatives based on given attributes from design requirements and weighting given by the decision-makers. Additionally, the other benefits of the proposed methodology are the elimination of a complex structure and/or a black-box algorithm. The fuzzy-TOPSIS approach capable of considering not only the uncertainty related to qualitative judgments but also the uncertainty that may reside in the measurement of quantitative or qualitative parameters that exist within the safety, health, and environment risks assessment. A numerical example of selecting material for automotive body panels using the proposed method has been discussed.

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use

Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images. To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software. This prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance. Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3-trained echocardiographers independently and blindly evaluated each acquisition. Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers. A total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters. This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.

Rock Burst Evaluation Using the CRITIC Algorithm-Based Cloud Model

Under high-stress conditions, rock burst disasters can significantly impact underground civil engineering construction. For underground metal mines, rock burst evaluations and prevention during mining have become major research topics, and the prediction and prevention of rock burst must be based on the study of rocks and rock burst tendencies. To further prevent the risk of geological disasters and provide timely warnings, a finite-interval cloud model based on the CRITIC algorithm is proposed in this paper to address the uncertainty of rock burst evaluation, the complexity under multi-factor interactions, and the correlations between factors, and it then realizes a preliminary qualitative judgment of rock burst disasters. This paper selects the uniaxial compressive strength σc (I1), ratio of the uniaxial compressive strength to the tensile strength σc/σt (brittleness coefficient, I2), elastic deformation energy index Wet (I3), ratio of the maximum tangential stress to the uniaxial compressive strength σθ /σc (stress coefficient, I4) of the rock, depth of the roadway H (I5), and integrity coefficient of the rock mass Kv (I6) as indicators for rock burst propensity predictions. The CRITIC algorithm is used to consider the relationships between the evaluation indicators, and it is combined with an improved cloud model to verify 20 groups of learning samples. The calculation results obtained by the prediction method are basically consistent with the actual situation. The validity of the model is tested, and then the model is applied to the Dongguashan Copper Mine in Tongling, Anhui Province, China, for rock burst evaluation.

Introducing Patents with Indirect Connection (PIC) for Establishing Patent Strategies

A patent system requires novelty and progressiveness so that new patents do not infringe on the rights of prior art. Patent investigation including a prior art search is essential to the process of commercialization of technology. In general, patent investigation has been conducted by experts based on their qualitative judgement. However, the number of patents has increased so fast that it has become difficult to handle the quantitative burdens of the search with a conventional approach. There have been previous studies dealing with patent investigation to find similar technologies. They had limitations as they did not utilize the citation relationship and similarity between patents in a comprehensive way. In addition, they could not properly reflect the sequential citation relationship of patents though this is effective in discovering similar patents. In this study, we propose an efficient methodology to discover similar technologies by comprehensively considering the similarity and citation relationship between patents. In particular, we intended to reflect the citation sequence and indirect citation relationship in the process of searching for similar patents. For this, we introduced the concept of “patents with indirect connections” (PICs) and devised an algorithm to efficiently detect patent pairs having such a relationship. The proposed methodology of this study contributes to preventing patent litigation in advance by discovering patents with such potential risks. It is expected that this method will provide patent applicants with the opportunity to establish appropriate strategies against competitors with similar technologies. In order to examine the practical applicability of the proposed method, Korean patents related to machine learning and deep learning were collected. As a result of the experiment, it was possible to identify 24 pairs of similar patents without a direct citation relationship and derive appropriate counter strategies.

Методика построения интегральных показателей комплексной оценки свойств узлов электроэнергетических систем

The problem of selecting the electric power system control nodes is studied. By performing control of these modes, matters concerned with providing reliable power supply of the required quality to consumers can be settled in the most efficient manner. As an example, a fragment of the electric power system mathematical model used in the Finist mode-setting simulator for a power system dispatch control center operator is considered, which represents a highly branched electrical network consisting of eleven 110 kV nodes, three 220 kV nodes connected with the system, and two generator nodes. A new procedure for selecting the control nodes is proposed, which takes into account a combination of different indicators having different measurement units, dimensions and scales is proposed. These indicators characterize the following properties of power system nodes: the reactive power fraction absorbed at a node, the sensitivity of voltage to reactive load variations, the number of connected power lines, and statistical indicators characterizing the change of voltage at the nodes and reactive power flows for different options of installing the reactive power compensation devices. For combined use of these indicators, they were ranked according to the efficiency of installing reactive power compensation devices in the system. For each indicator, a scale of five ranks (intervals) is set, which determine the preferences (qualitative judgments) of the researcher in evaluating the reactive power compensation devices installation efficiency at the system nodes. The highest rank (5) corresponds to the maximum efficiency, and the lowest rank (1) corresponds to the minimum efficiency. To calculate the individual (integral) priority indicator of installing reactive power compensation devices, the ranks of indicators are added together, and their sum is divided by the product of the number of ranks by the number of the used indicators (features). Based on the calculation results, the rating (location) of each node is determined, and the nodes for installing the reactive power compensation devices are selected according to their effect on ensuring the electric power system operation reliability, active power losses in the network, and voltage regulation. Thus, a new procedure is presented for determining the integral indicators for comprehensively estimating the properties of complex electric power system nodes and selecting the controlled nodes using a system of various indicators. These indicators characterize the studied nodes in terms of the efficiency of installing reactive power compensation devices to reduce active power losses in the network, voltage regulation, and ensuring the electric power system operational reliability. The validity of the results obtained in the study is confirmed by their comparison with the indicators of the balance-conductivity method, which has proven itself in solving problems connected with determining the nodes for controlling electric power system operation modes.

A Hybrid Approach to Building Peer Groups for Technology Evaluation to Generate Reference Information

Technology evaluation facilitates successful implementation of a technology financing system. It is usually done with a combined approach of data-driven quantitative evaluation and expert-driven qualitative evaluation due to the labyrinthine nature of the technology, but largely relies on the qualitative judgement of experts. It naturally causes a problem that the evaluation result for the same technology varies considerably depending on who evaluates it. To enable consistent technology evaluation, a few methods have been presented to generate reference information for technology evaluation by identifying relevant firms. However, they cannot explore the detailed properties of individual technologies when building peer groups with the identified relevant firms. Since technology is the ultimate subject of evaluation, not only the similarity between firms, but also the similarity between technologies should be investigated in the process of identifying relevant firms. Therefore, this study proposes a hybrid approach, which builds peer groups by measuring both the similarities between firms and between technologies and generates reference information that enables efficient and consistent technology evaluation. It is quite common for several evaluators to be involved in technology evaluation. If the evaluation results are inconsistent, additional manual work should be performed to reconcile these inconsistent results. Therefore, the proposed approach can improve the efficiency of the technology evaluation activities by avoiding unnecessary manual work. Furthermore, by providing useful reference information to the evaluator in an automated way, it will help maintain the consistency of the evaluation result so that the result does not vary greatly depending on the evaluators.

Rapid qualitative and quantitative detection of formaldehyde in squids based on colorimetric sensor array

The colorimetric sensor array was used to detect the volatile organic compounds (VOCs) in squids with different formaldehyde content. In order to distinguish whether the formaldehyde is artificially added in the squids, the linear discriminant analysis (LDA) and K-nearest neighbor (KNN) based on principal component analysis (PCA) were used to make qualitative judgments, the result shows that the recognition rates of the training set and prediction set of the LDA model were 95% and 85% respectively, and the recognition rates of the training set and prediction set of the KNN model were both 90%. Moreover, error back propagation artificial neural network (BP-ANN) was used to quantitatively predict the concentration of formaldehyde in squids. The result indicates that the BP-ANN model acquired a good recognition rate with the correlation coefficient (Rp) for prediction was 0.9887 when the PCs was 10. To verify accuracy and applicability of the model, paired sample t-test was used to verify the difference between the predicted value of formaldehyde in the BP-ANN model and the actual addition amount. Therefore, this approach showed well potentiality to provide a fast, accuracy, no need for a pretreatment, and low-cost technique for detecting the formaldehyde in squids.

Пакетный вейвлет-анализ торкрет-каменных структур с использованием импакт-эхометода

In this study, the contact quality of shotcrete-rock structures is studied using the impact-echo method (IE) to detect defects in shotcrete with a thickness of 10 and 20 cm. Numerical and experimental studies were conducted. Traditional frequency-domain analysis can only provide a qualitative judgment, but it fails to quantitatively estimate the severity of defect on the contact surface. Wavelet packet decomposition is used to analyze the signal to obtain the distribution of energy in each frequency band. The relative energy eigenvalues of the wavelet packet were calculated later, and the energy characteristic index of shotcrete with different thicknesses and bonding conditions was obtained. An interface quality evaluation approach based on energy was provided to quantitatively evaluate the contact quality at the interface of the shotcrete-rock structures.

A multi-criteria decision model based on the evidential reasoning approach for the selection of fulcrum ports supporting Arctic shipping through the Northeast Passage

ABSTRACT As a result of the opening Arctic waters, Arctic Northeast Passage (NEP) can be used as a shortcut between Asian and European ports, which may bring considerable economic benefits. Due to the harsh environment and substantial costs, it is difficult to construct many fulcrum ports along the Russian Arctic coast, which provide support services for ships navigating the NEP. A multi-criteria decision model is hence constructed to select fulcrum ports among candidates to support Arctic shipping through the NEP. Based on the model, the evidential reasoning (ER) approach is used to aggregate quantitative data and qualitative expert judgments to generate distributed assessments for candidate ports, as well as modeling uncertainties such as incompleteness and ignorance in the assessment of candidate ports. Their suitability as fulcrum ports are prioritized according to the utilities. The results show that Pevek, Sabetta, and Murmansk are the ideal fulcrum ports for the eastern, central, and western section, respectively, of the Russian Arctic coast. The associated policy recommendations include prioritizing the improvement of selected fulcrum ports, attaching importance to promising candidate ports, updating outdated infrastructure, and implementing more related projects. A sensitivity analysis conducted in this study validates the feasibility and reasonability of the proposed model.

The potential use of Bayesian Networks to support committee decisions in programmatic assessment

The benefits of programmatic assessment are well-established. Evidence from multiple assessment formats is accumulated and triangulated to inform progression committee decisions. Committees are consistently challenged to ensure consistency and fairness in programmatic deliberations. Traditional statistical and psychometric techniques are not well-suited to aggregating different assessment formats accumulated over time. Some of the strengths of programmatic assessment are also vulnerabilities viewed through this lens. While emphasis is often placed on data richness and considered input of qualified experts, committees reasonably wish for practical, defensible solutions to these challenges. We draw upon on existing literature regarding Bayesian Networks (BN), noting their utility and application in educational systems. We provide illustrative examples of how they could potentially be used in contexts that embed programmatic principles. We show a simple BN for a knowledge domain before presenting a full-scale 'proof of concept' BN to support committee decisions. We zoom in on one 'node' to demonstrate the capacity of incorporating disparate evidence throughout the network. Bayesian Networks offer an approach that is theoretically well-supported for programmatic assessment. They can aid committees in managing evidence accumulation, help them make inferences under conditions of uncertainty, and buttress decisions by adding a layer of defensibility to the process. They are a pragmatic tool adding value to the programmatic space by applying a complementary statistical framework. We see four major benefits of BNs in programmatic assessment: BNs allow for visual capturing of evidentiary arguments by committees during decision-making; 'recommendations' from probabilistic pathways can be used by committees to confirm their qualitative judgments; BNs can ensure precedents are maintained and consistency occurs over time; and the imperative to capture data richness is maintained without resorting to questionable methodological strategies such as adding qualitatively different things together. Further research into their feasibility and robustness in practice is warranted.

Ranking of author assessment parameters using Logistic Regression

The renowned international scientific societies nominate researchers for awards based on qualitative judgments every year. Qualitative judgment uses subjective assessments based on information that is not quantifiable. The way of assessing the quality of the work has not been established or disclosed, nor do we have any qualitative evaluation criteria. We can assess the quality of the researcher's work by mapping the quantitative parameters to qualitative judgments. To date, the scientific community has presented more than 50 research assessment quantitative parameters, including publication count, citation count, h-index, and its variants. The contemporary state-of-the-art in authors ranking does not determine the best parameter that effectively maps on experts' qualitative evaluation. Moreover, these parameters have been evaluated by using same scenarios. In such scenarios, the value and effect of each parameter over the others are complicated to ascertain. Therefore, they must be assessed in inequitable scenarios. The purpose of this research is to identify the significant parameters that map on qualitative judgments of international scientific societies in Civil Engineering (CE) for award nominations. We will identify the rank of author assessment parameters, which includes published papers, citations, No of years since 1st publication, citations in h-core, authors/paper, citations/paper, citations/year, h-index, g-index, hg-index, A-index, R-index, e-index, and f-index. We have evaluated these parameters on the dataset from the discipline of Civil Engineering (CE). The data set contains 250 non-award winners and 250 award winners from prestigious scientific societies of CE. The h-index and its variants have been ranked based on their effectiveness for awardees using Logistic Regression. The award-winning researchers have less number of average authors/paper than the non-awardees. The authors/paper has achieved the highest effectiveness of 67% for awardees. Furthermore, we have also analyzed the ratio of awardees in the ranked list of 50, 100, and 150 researchers by author assessment parameters. The authors/papers have outperformed all other indices by elevating 62% and 66% of the award recipients in its ranked list of 100 and 150 researchers. In the ranked list of 50 researchers, publications elevate 54% awardees, and Authors/papers achieved the second-highest elevation score of awardees of 50%.