Explicit Relevance Research Articles

Relevance Feedback with Brain Signals

The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on the user, extensive studies have explored using pseudo-relevance signals and implicit feedback signals as substitutes. However, such signals are indirect indicators of relevance and suffer from complex search scenarios where user interactions are absent or biased. Recently, the advances in portable and high-precision brain-computer interface (BCI) devices have shown the possibility to monitor user’s brain activities during search process. Brain signals can directly reflect user’s psychological responses to search results and thus it can act as additional and unbiased RF signals. To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based RF with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking. The experimental results on the user study dataset show that incorporating brain signals leads to significant performance improvement in our RF framework. Besides, we observe that brain signals perform particularly well in several hard search scenarios, especially when implicit signals as feedback are missing or noisy. This reveals when and how to exploit brain signals in the context of RF.

Impact of Covid-19 Pandemic on User Search Behavior: A Case Study of Postgraduate Students

Relevance Feedback (RF) is crucial for building a user profile which is a fundamental element of different intelligent systems such as information retrieval, information filtering, and personalization. RF is affected by a number of contextual factors such as mood, stress level, and sentimental state of the user. Covid-19 pandemic imposed dramatic changes to the user environment as well as the search context. This paper investigates user’s search behaviour to identify the differences in the behavior between the contexts before and during the Covid-19 pandemic. This can be practically translated into identifying the differences in the relationship between the implicit feedback and the explicit relevance level between the two contexts. For this purpose, we conducted three user studies (i) Pre-COVID-19, (ii) Mid COVID-19 and (iii) after Covid-19. A user study was conducted on the same group of users on the three user studies. The Pre-COVID-19 user study took place before the pandemic started and the Mid-COVID-19 user study took place three months after the beginning of the pandemic. After Covid-19 stage took place after 18 months of the pandemic. A linear regression model was developed for each user study using IBM-SPSS. The analysis showed a significant variation in the user behavior between the two studies due to the COVID-19 context and its impact on user search behaviour. Also, two new RF parameters in Mid-COVID-19 were shown to have a significant relationship with the explicit user interest which were Mouse Clicks and Page/Down strikes. Furthermore, the comparison between the two models showed that the second regression model achieved a higher accuracy level that is attributed to the common behavioral change imposed by the pandemic.

AnANet: Association and Alignment Network for Modeling Implicit Relevance in Cross-Modal Correlation Classification

With the explosive increase of multimodal data, cross-modal correlation classification has become an important research topic and is in great demand in many cross-modal applications. A variety of classification schemes and predictive models have been built based on the existing cross-modal correlation categorization. However, these classification schemes typically follow the prior assumption that the paired cross-modal samples are strictly related, and thus pay great attention to the fine-grained relevant types of cross-modal correlation, ignoring the high volume of implicitly relevant data which are often wrongly classified into irrelevant types. Even more, previous predictive models fall short of reflecting the essence of cross-modal correlation according to their definitions, especially in the modeling of network structure. Thus in this paper, by comprehensively investigating the current image-text correlation classification research, we redefine a new classification scheme for cross-modal correlation based on the implicit and explicit relevance. To predict the types of image-text correlation based on our proposed definition, we further devise the Association and Alignment Network (namely AnANet) to model the implicit and explicit relevance, which captures both the implicit association of global discrepancy and commonality between image and text and explicit alignment of cross-modal local relevance. Experimental studies on our constructed new image-text correlation dataset verify the effectiveness of our proposed model.

Unified Adaptive Relevance Distinguishable Attention Network for Image-Text Matching

Image-text matching, as a fundamental cross-modal task, bridges the gap between vision and language. The core is to accurately learn semantic alignment to find relevant shared semantics in image and text. Existing methods typically attend to all fragments with word-region similarity greater than empirical threshold zero as relevant shared semantics, e.g., via a ReLU operation that forces the negative to zero and maintains the positive. However, this fixed threshold is totally isolated with feature learning, which cannot adaptively and accurately distinguish the varying distributions of relevant and irrelevant word-region similarity in training, inevitably limiting the semantic alignment learning. To solve this issue, we propose a novel Unified Adaptive Relevance Distinguishable Attention (UARDA) mechanism, incorporating the relevance threshold into a unified learning framework, to maximally distinguish the relevant and irrelevant distributions to obtain better semantic alignment. Specifically, our method adaptively learns the optimal relevance boundary between these two distributions to improve the model to learn more discriminative features. The explicit relevance threshold is well integrated into similarity matching, which kills two birds with one stone as: (1) excluding the disturbances of irrelevant fragment contents to aggregate precisely relevant shared semantics for boosting matching accuracy, and (2) avoiding the calculation of irrelevant fragment queries for reducing retrieval time. Experimental results on benchmarks show that UARDA can substantially and consistently outperform state-of-the-arts, with relative rSum improvements of 2%-4% (16.9%-35.3% for baseline SCAN), and reducing the retrieval time by 50%-73%.

A Lower-Class Advantage in Face Memory.

People remember what they deem important. In line with research suggesting that lower-class (vs. higher class) individuals spontaneously appraise other people as more relevant, we show that social class is associated with the habitual use of face memory. We find that lower-class (vs. higher class) participants exhibit better incidental memory for faces (i.e., spontaneous memory for faces they had not been instructed to memorize; Studies 1 and 2). No social-class differences emerge for faces participants are instructed to learn (Study 2), suggesting that this pattern reflects class-based relevance appraisals rather than memory ability. Study 3 extends our findings to eyewitness identification. Lower-class (vs. higher-class) participants' eyewitness accuracy is less impacted by the explicit relevance of a target (clearly relevant thief vs. incidental bystander). Integrative data analysis shows a robust negative association between social class and spontaneous face memory. Preregistration (Studies 1 and 3) and cross-cultural replication (Study 2) further strengthen the results.

Bi-CMR: Bidirectional Reinforcement Guided Hashing for Effective Cross-Modal Retrieval

Cross-modal hashing has attracted considerable attention for large-scale multimodal data. Recent supervised cross-modal hashing methods using multi-label networks utilize the semantics of multi-labels to enhance retrieval accuracy, where label hash codes are learned independently. However, all these methods assume that label annotations reliably reflect the relevance between their corresponding instances, which is not true in real applications. In this paper, we propose a novel framework called Bidirectional Reinforcement Guided Hashing for Effective Cross-Modal Retrieval (Bi-CMR), which exploits a bidirectional learning to relieve the negative impact of this assumption. Specifically, in the forward learning procedure, we highlight the representative labels and learn the reinforced multi-label hash codes by intra-modal semantic information, and further adjust similarity matrix. In the backward learning procedure, the reinforced multi-label hash codes and adjusted similarity matrix are used to guide the matching of instances. We construct two datasets with explicit relevance labels that reflect the semantic relevance of instance pairs based on two benchmark datasets. The Bi-CMR is evaluated by conducting extensive experiments over these two datasets. Experimental results prove the superiority of Bi-CMR over four state-of-the-art methods in terms of effectiveness.

Contrastive Personalization Approach to Suspect Identification (Student Abstract)

Targeted image retrieval has long been a challenging problem since each person has a different perception of different features leading to inconsistency among users in describing the details of a particular image. Due to this, each user needs a system personalized according to the way they have structured the image in their mind. One important application of this task is suspect identification in forensic investigations where a witness needs to identify the suspect from an existing criminal database. Existing methods require the attributes for each image or suffer from poor latency during training and inference. We propose a new approach to tackle this problem through explicit relevance feedback by introducing a novel loss function and a corresponding scoring function. For this, we leverage contrastive learning on the user feedback to generate the next set of suggested images while improving the level of personalization with each user feedback iteration.

Relevance assessments, bibliometrics, and altmetrics: a quantitative study on PubMed and arXiv

Relevance is a key element for analyzing bibliometrics and information retrieval (IR). In both domains, relevance decisions are discussed theoretically and sometimes evaluated in empirical studies. IR research is often based on test collections for which explicit relevance judgments are made, while bibliometrics is based on implicit relevance signals like citations or other non-traditional quantifiers like altmetrics. While both types of relevance decisions share common concepts, it has not been empirically investigated how they relate to each other on a larger scale. In this work, we compile a new dataset that aligns IR relevance judgments with traditional bibliometric relevance signals (and altmetrics) for life sciences and physics publications. The dataset covers PubMed and arXiv articles, for which relevance judgments are taken from TREC Precision Medicine and iSearch, respectively. It is augmented with bibliometric data from the Web of Science and Altmetrics. Based on the reviewed literature, we outline a mental framework supporting the answers to our research questions. Our empirical analysis shows that bibliometric (implicit) and IR (explicit) relevance signals are correlated. Likewise, there is a high correlation between biblio- and altmetrics, especially for documents with explicit positive relevance judgments. Furthermore, our cross-domain analysis demonstrates the presence of these relations in both research fields.

L’identification des arguments et la hiérarchisation des marges : critères formels et critères conceptuels

The distinction between arguments and margins within a simple nuclear sentence is sharp at conceptual level in that it is grounded in explicit relevance criteria: arguments are saturated referential noun phrases that are essential for the integrity of the process; different layers of margins enrich different kinds of processes according to different consistency requirements. If one observes the syntactic structure of linguistic expressions, on the other hand, the same distinction seems to shade into a sort of continuum owing to two orders of factors. First, there is a cleavage between the model sentence, whose main function should be the expression of the process, and the utterances actually documented in texts and corpora, whose structure is shaped by the incommensurate function to adapt the structure of the process to the communicative dynamism of a text. Moreover, within the model sentence itself, the coding regime of arguments and the coding regime of margins shadow into one another: some margins are coded, like arguments, through formal grammatical relations, while some arguments are coded, like margins, directly as conceptual relations through a set of forms of expression motivated by their conceptual content.In spite of these obstacles, the conceptual distinction between arguments and margins and the hierarchy of margins can be identified at the level of model sentence thanks to adequate and differentiated criteria. These criteria are formal where the difference of coding regime draws a sharp formal distinction between arguments and margins, and conceptual and textual where the structure of the forms of expression neutralises the distinction. Conceptual and textual criteria also make the identification of a clear hierarchy of margins possible.

Recommendations for fluid management of adults with sepsis in sub-Saharan Africa: a systematic review of guidelines

BackgroundSepsis guidelines are widely used in high-income countries and intravenous fluids are an important supportive treatment modality. However, fluids have been harmful in intervention trials in low-income countries, most notably in sub-Saharan Africa. We assessed the relevance, quality and applicability of available guidelines for the fluid management of adult patients with sepsis in this region.MethodsWe identified sepsis guidelines by systematic review with broad search terms, duplicate screening and data extraction. We included peer-reviewed publications with explicit relevance to sepsis and fluid therapy. We excluded those designed exclusively for specific aetiologies of sepsis, for limited geographic locations, or for non-adult populations. We used the AGREE II tool to assess the quality of guideline development, performed a narrative synthesis and used theoretical case scenarios to assess practical applicability to everyday clinical practice in resource-constrained settings.ResultsPublished sepsis guidelines are heterogeneous in sepsis definition and in quality: 8/10 guidelines had significant deficits in applicability, particularly with reference to resource considerations in low-income settings. Indications for intravenous fluid were hypotension (8/10), clinical markers of hypoperfusion (6/10) and lactataemia (3/10). Crystalloids were overwhelmingly recommended (9/10). Suggested volumes varied; 5/10 explicitly recommended “fluid challenges” with reassessment, totalling between 1 L and 4 L during initial resuscitation. Fluid balance, including later de-escalation of therapy, was not specifically described in any. Norepinephrine was the preferred initial vasopressor (5/10), specifically targeted to MAP > 65 mmHg (3/10), with higher values suggested in pre-existing hypertension (1/10). Recommendations for guidelines were almost universally derived from evidence in high-income countries. None of the guidelines suggested any refinement for patients with malnutrition.ConclusionsWidely used international guidelines contain disparate recommendations on intravenous fluid use, lack specificity and are largely unattainable in low-income countries given available resources. A relative lack of high-quality evidence from sub-Saharan Africa increases reliance on recommendations which may not be relevant or implementable.

No evidence for an effect of explicit relevance instruction on consolidation of associative memories

Newly encoded memories are stabilized over time through a process or a set of processes termed consolidation, which happens preferentially during sleep. However, not all memories profit equally from this offline stabilization. Previous research suggested that one factor, which determines whether a memory will benefit from sleep consolidation, is future relevance. The aim of our current study was to replicate these findings and expand them to investigate their neural underpinnings.In our experiment, 38 participants learned two sets of object-location associations. The two sets of stimuli were presented to each participant intermixed and in random order. After study, participants performed a baseline retention test and were thereafter instructed that, after a delay containing sleep, they would be tested and rewarded only on one of the two sets of stimuli. This relevance instruction was revoked, however, immediately before the test. Thus, this manipulation affected memory consolidation while having no influence on encoding and retrieval. This retention interval was monitored via actigraphy recordings.While the study session was purely behavioral, the test session was conducted in an MRI scanner, thus we collected neuroimaging data at retrieval of relevant compared with non-relevant items.Behaviorally, we found no effect of the relevance manipulation on memory retention, confidence rating, or reaction time. At a neural level, no effect of relevance on memory retrieval-related brain operations was observed. Contrary to our expectations, the relevance manipulation did not result in improved consolidation, nor in improved subsequent performance at retrieval. These findings challenge previously published results and suggest that future relevance as manipulated here may not be sufficient to produce enhanced memory consolidation.

Resistance of curved surfaces to the cavitation erosion produced through high-pressure submerged waterjet

The aim of the present study is to reveal fundamental cavitation erosion characteristics of the curved surface. The submerged waterjet was used to produce cavitation. An experimental work was performed on a standard cavitation erosion test rig. The Al 5083 specimens with three curvature radii, 7.0, 9.0 and 10.0 mm, were selected as the target specimens. The flat surfaces were tested for comparison. The mass loss of the eroded specimens was measured. The cumulative erosion rate was calculated. Morphology of the eroded surfaces was observed using techniques of scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM). The root mean square (RMS) surface roughness of the eroded specimen was obtained. The results indicate that the curvature radius has a significant effect on the cavitation erosion intensity. The surface with the largest radius of curvature exhibits the weakest resistance to the cavitation erosion. No explicit relevance is recognized between the curvature radius and the extent of the cavitation erosion. In the early stage of the cavitation erosion, the central region of the target surface is less eroded. As the cavitation erosion progresses, the mass removal in the central area manifests a layer-by-layer pattern. Large erosion pits are evidenced in such a region. The specimen of R = 9.0 mm, with the highest resistance to the cavitation erosion, is featured by a sharp decline of the skewness of surface height distribution in the early stage of the cavitation erosion.

Attentional allocation to task-irrelevant fearful faces is not automatic: experimental evidence for the conditional hypothesis of emotional selection

ABSTRACTA growing body of research indicates that attentional biases toward emotional stimuli are not automatic, but may depend on the relevance of emotion to the top-down search goals of the observer. To determine whether and how this relevance factor modulates attentional allocation to irrelevant fearful faces, four spatial cueing tasks were designed, in which the goal-relevance of completely task-irrelevant (neutral or fearful) cue faces was systematically manipulated by changing the target defining feature. No attentional capture by cue faces (be they neutral or fearful) was observed when the cue faces were completely goal-irrelevant. When faces – but not facial expressions – were goal-relevant, fearful cue faces captured attention, but so did neutral cue faces to a similar extent. Only when facial expressions were explicitly goal-relevant did we observe a difference between attentional allocation to fearful and neutral cue faces, with larger cueing effects for neutral cue faces in the Neutral task, and for fearful cue faces in the Fearful task. Therefore, rather than automatic, attentional allocation to irrelevant fearful faces proved conditional to the explicit relevance of fearful expressions to top-down search goals.

Effect of heat treatment on the cavitation erosion resistance of stainless steel

To evaluate the effect of heat treatment on the cavitation erosion resistance of stainless steel, an experimental investigation is carried out using a cavitating jet test apparatus complying with the ASTM G134 standard. The jet pressure is 30 MPa and the cavitation number is 0.014. Specimens made of stainless steel SUS630 are aged at different temperatures. Prior to cavitation erosion experiment, the mechanical properties of the specimens are obtained through an inverse analysis method in conjunction with the indentation test. The material's cavitation erosion resistance is represented by the reciprocal of the maximum cumulative mass loss rate. The correlation between cavitation erosion resistance and mechanical parameters is pursued. The variation of cumulative mass loss with the exposure time under cavitating jet impact demonstrates that the rise of aging temperature impairs the cavitation erosion resistance of SUS630. The overall cumulative mass loss rate of SUS630 is lower than that of SUS316L. The correlations between cavitation erosion resistance and mechanical parameters are evident. The parameter of microhardness exhibits the most explicit relevance with respect to the cavitation erosion resistance.

Toward the Development of a Marine Administration System Based on International Standards

The interests, responsibilities and opportunities of states to provide infrastructure and resource management are not limited to their land territory but extend to marine areas as well. So far, although the theoretical structure of a Marine Administration System (MAS) is based on the management needs of the various countries, the marine terms have not been clearly defined. In order to define an MAS that meets the spatial marine requirements, the specific characteristics of the marine environment have to be identified and integrated in a management system. Most publications that address the Marine Cadastre (MC) concept acknowledge the three-dimensional (3D) character of marine spaces and support the need for MC to function as a multipurpose instrument. The Land Administration Domain Model (LADM) conceptual standard ISO 19152 has been referenced in scholarly and professional works to have explicit relevance to 3D cadastres in exposed land and built environments. However, to date, very little has been done in any of those works to explicitly and comprehensively apply LADM to specific jurisdictional MAS or MC, although the standard purports to be applicable to those areas. Since so far the most comprehensive MC modeling approach is the S-121 Maritime Limits and Boundaries (MLB) Standard, which refers to LADM, this paper proposes several modifications including, among others, the introduction of class marine resources into the model, the integration of data on legal spaces and physical features through external classes, as well as the division of law and administrative sources. Within this context, this paper distinctly presents both appropriate modifications and applications of the IHO S-121 standard to the particular marine and maritime administrative needs of both Greece and the Republic of Trinidad and Tobago.

Textual and graphical refutations: Effects on conceptual change learning

Refutation text is potentially more effective than standard text for conceptual change. Learning from text and graphic is also potentially superior to learning from text alone. In two studies, we investigated the effectiveness of both a refutation text and a refutation graphic for promoting high school students’ conceptual change learning about season change, as well as their metacognitive awareness of conceptual conflict and knowledge revision. In both studies, participants were randomly assigned to one of four conditions: (1) standard text with standard graphic, (2) standard text with refutation graphic, (3) refutation text with standard graphic, or (4) refutation text with refutation graphic. Both studies had a pretest, immediate post-test, and delayed post-test design and involved students with an initial common misconception about the causes of season change. In Study 2, explicit relevance instructions to observe the important illustration were given to the participants. In both studies, refutation text with refutation graphic was not more beneficial than other instructional materials, either at immediate or delayed post-test. In Study 1, more stable conceptual change learning emerged in readers of the refutation text with standard graphic compared to readers in the control condition. In Study 2, readers of the standard text with refutation graphic performed as well as readers of the refutation text with standard graphic. In addition, more readers of the refutation text with either graphic showed metacognitive awareness of their knowledge change compared to readers in the control condition. Educational implications underline the importance of relevance instructions for guiding readers toward the graphic and of the design of text-graphic pairing to sustain knowledge revision.

Diversity-oriented Multimodal and Interactive Information Retrieval

Information retrieval methods, especially considering multimedia data, have evolved towards the integration of multiple sources of evidence in the analysis of the relevance of the items considering a given user search task. In this context, for attenuating the semantic gap between low-level features extracted from the content of the digital objects and high-level semantic concepts (objects, categories, etc.) and making the systems adaptive to different user needs, interactive models have brought the user closer to the retrieval loop allowing user-system interaction mainly through implicit or explicit relevance feedback. Analogously, diversity promotion has emerged as an alternative for tackling ambiguous or underspecified queries. Additionally, several works have addressed the issue of minimizing the required user effort on providing relevance assessments while keeping an acceptable overall effectiveness This thesis discusses, proposes, and experimentally analyzes multimodal and interactive diversity-oriented information retrieval methods. This work, comprehensively covers the interactive information retrieval literature and also discusses about recent advances, the great research challenges, and promising research opportunities. We have proposed and evaluated two relevancediversity trade-off enhancement work-flows, which integrate multiple information from images, such as: visual features, textual metadata, geographic information, and user credibility descriptors. In turn, as an integration of interactive retrieval and diversity promotion techniques, for maximizing the coverage of multiple query interpretations/aspects and speeding up the information transfer between the user and the system, we have proposed and evaluated a multimodal online learning-to-rank method trained with relevance feedback over diversified results Our experimental analysis shows that the joint usage of multiple information sources positively impacted the relevance-diversity balancing algorithms. Our results also suggest that the integration of multimodal-relevance-based filtering and reranking is effective on improving result relevance and also boosts diversity promotion methods. Beyond it, with a thorough experimental analysis we have investigated several research questions related to the possibility of improving result diversity and keeping or even improving relevance in interactive search sessions. Moreover, we analyze how much the diversification effort affects overall search session results and how different diversification approaches behave for the different data modalities. By analyzing the overall and per feedback iteration effectiveness, we show that introducing diversity may harm initial results whereas it significantly enhances the overall session effectiveness not only considering the relevance and diversity, but also how early the user is exposed to the same amount of relevant items and diversity

Using low‐cost electroencephalography (EEG) sensor to identify perceived relevance on web search

ABSTRACTThis poster presents preliminary results from a user study designed to evaluate the feasibility to use a low‐cost EEG sensor in the identification of information relevance as perceived by users. The study involved 10 participants, both graduate and undergraduate students, performing a self‐motivated exploratory search task contextualized on the literature review for their own thesis work. The study design comprised two stages that focus on (1) snippets collection and (2) explicit relevance assessments. In both stages, participants wore a low‐cost electroencephalography (EEG) sensor that provides measures related to two mental states (i.e. attention and meditation). Analyses focused on comparing the presence and intensity of these mental states in the set of pages (both relevant and non‐relevant) classified by the users themselves. Results showed that attention levels and blink intensity in relevant pages are significantly higher than in non‐relevant ones.

A learning to rank approach for quality‐aware pseudo‐relevance feedback

Pseudo relevance feedback (PRF) has shown to be effective in ad hoc information retrieval. In traditional PRF methods, top‐ranked documents are all assumed to be relevant and therefore treated equally in the feedback process. However, the performance gain brought by each document is different as showed in our preliminary experiments. Thus, it is more reasonable to predict the performance gain brought by each candidate feedback document in the process of PRF. We define the quality level (QL) and then use this information to adjust the weights of feedback terms in these documents. Unlike previous work, we do not make any explicit relevance assumption and we go beyond just selecting “good” documents for PRF. We propose a quality‐based PRF framework, in which two quality‐based assumptions are introduced. Particularly, two different strategies, relevance‐based QL (RelPRF) and improvement‐based QL (ImpPRF) are presented to estimate the QL of each feedback document. Based on this, we select a set of heterogeneous document‐level features and apply a learning approach to evaluate the QL of each feedback document. Extensive experiments on standard TREC (Text REtrieval Conference) test collections show that our proposed model performs robustly and outperforms strong baselines significantly.

Are leadership values different across generations?

Purpose – The popular use of labels such as Baby Boomers, Generation X, Generation Y and Millennials suggests that the nature of effective leadership changes over time in response to the prevailing modern context. Using a values-based leadership lens, defined as the moral foundation underlying stewardship decisions and actions of leaders, the purpose of this paper is to explore the alternative notion that fundamental leadership ideals – from antiquity to modern executives to MBA students – are timeless in nature. Design/methodology/approach – Using a thematic analysis approach, The Aeneid was coded for key leadership themes (integrity, good judgment, leadership by example, decision-making, trust, justice/fairness, humility, and sense of urgency); and a mixed-method research framework was employed to juxtapose the leadership lessons identified to the demands of modern leadership. Deductive thematic analysis was utilized to examine key themes from responses of 13 multi-sectoral leaders (for profit, non-profit, government) and 137 MBA students (from three MBA programs in differing regions). Findings – Whether viewed qualitatively or quantitatively, or across sectors, the findings of this study affirm the explicit relevance of The Aeneid to the demands of modern leadership. Additionally, it was found that the way managers ranked leadership values was not significantly different from how MBA students ranked the same values. Moreover, the authors found integrity to be a superordinate value – without which the remaining values have far less significance. Originality/value – This research highlights a leadership paradox – while managerial traits are an important consideration for the prevailing operational context in the short term, a values-based approach to hiring, promoting and retaining leaders may be superior in achieving organizational sustainability and performance. This study illustrates the practical contemporary relevance of The Aeneid specifically, and illustrates a humanities laden and values-based approach to reflecting on leadership effectiveness generally.