Local Translation Research Articles

Does Quantum Mechanics Require "Conspiracy"?

Quantum states containing records of incompatible outcomes of quantum measurements are valid states in the tensor-product Hilbert space. Since they contain false records, they conflict with the Born rule and with our observations. I show that excluding them requires a fine-tuning to an extremely restricted subspace of the Hilbert space that seems "conspiratorial", in the sense that (1) it seems to depend on future events that involve records (including measurement settings) and on the dynamical law (normally thought to be independent of the initial conditions), and (2) it violates Statistical Independence, even when it is valid in the context of Bell's theorem. To solve the puzzle, I build a model in which, by changing the dynamical law, the same initial conditions can lead to different histories in which the validity of records is relative to the new dynamical law. This relative validity of the records may restore causality, but the initial conditions still must depend, at least partially, on the dynamical law. While violations of Statistical Independence are often seen as non-scientific, they turn out to be needed to ensure the validity of records and our own memories and, by this, of science itself. A Past Hypothesis is needed to ensure the existence of records and turns out to require violations of Statistical Independence. It is not excluded that its explanation, still unknown, ensures such violations in the way needed by local interpretations of quantum mechanics. I suggest that an as-yet unknown law or superselection rule may restrict the full tensor-product Hilbert space to the very special subspace required by the validity of records and the Past Hypothesis.

Chloroplast biogenesis involves spatial coordination of nuclear and organellar gene expression in Chlamydomonas.

The localization of translation can direct the polypeptide product to the proper intracellular compartment. Our results reveal translation by cytosolic ribosomes on a domain of the chloroplast envelope in the unicellular green alga Chlamydomonas (Chlamydomonas reinhardtii). We show that this envelope domain of isolated chloroplasts retains translationally active ribosomes and mRNAs encoding chloroplast proteins. This domain is aligned with localized translation by chloroplast ribosomes in the translation zone, a chloroplast compartment where photosystem subunits encoded by the plastid genome are synthesized and assembled. Roles of localized translation in directing newly synthesized subunits of photosynthesis complexes to discrete regions within the chloroplast for their assembly are suggested by differences in localization on the chloroplast of mRNAs encoding either subunit of the light-harvesting complex II or the small subunit of Rubisco. Transcription of the chloroplast genome is spatially coordinated with translation, as revealed by our demonstration of a subpopulation of transcriptionally active chloroplast nucleoids at the translation zone. We propose that the expression of chloroplast proteins by the nuclear-cytosolic and organellar genetic systems is organized in spatially aligned subcompartments of the cytoplasm and chloroplast to facilitate the biogenesis of the photosynthetic complexes.

Exploring local interpretability in dimensionality reduction: Analysis and use cases

Dimensionality reduction is a crucial area in artificial intelligence that enables the visualization and analysis of high-dimensional data. The main use of dimensionality reduction is to lower the dimensional complexity of data, improving the performance of machine learning models. Non-linear dimensionality reduction approaches, which provide higher quality representations than linear ones, lack interpretability, prohibiting their application in tasks requiring interpretability. This paper presents LXDR (Local eXplanation of Dimensionality Reduction), a local, model-agnostic technique that can be applied to any DR technique. LXDR trains linear models around a neighborhood of a specific instance and provides local interpretations using a variety of neighborhood generation techniques. Variations of the proposed technique are also introduced. The effectiveness of LXDR’s interpretations is evaluated by quantitative and qualitative experiments, as well as demonstrations of its practical implementation in diverse use cases. The experiments emphasize the importance of interpretability in dimensionality reduction and how LXDR reinforces it.

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers.

Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictions. However, the lack of interpretability within these ML models raises concerns about their inner workings and reliability. This paper introduces an innovative hybrid architecture, the TCN-LSTM-Multihead-Attention model, which combines two layers of temporal convolutional networks (TCN) followed by one layer of long short-term memory (LSTM) units, integrated with a Multihead-Attention mechanism for predicting streamflow with streamflow causation-driven prediction samples (RCDP), employing local and global interpretability studies through Shapley values and partial dependency analysis. The find_peaks method was used to identify peak flow events in the test dataset, validating the model's generality and uncovering the physical causative patterns of streamflow. The results show that (1) compared to the LSTM model with the same hyperparameter settings, the proposed TCN-LSTM-Multihead-Attention hybrid model increased the R2 by 52.9%, 2.5%, 43.1%, and 10.7% respectively at four stations in the test set predictions using RCDP samples. Moreover, comparing the prediction results of the hybrid model under different samples in Hengshan station, the R2 for RCDP increased by 5.06% and 1.22% compared to streamflow autoregressive prediction samples (RAP) and meteorological-soil volumetric water content coupled autoregressive prediction samples (MCSAP) respectively. (2) Historical streamflow data from the preceding 3days predominantly influences predictions due to strong autocorrelation, with flow quantity (Q) typically emerging as the most significant feature alongside precipitation (P), surface soil moisture (SSM), and adjacent station flow data. (3) During periods of low and normal flow, historical data remains the most crucial factor; however, during flood periods, the roles of upstream inflow and precipitation become significantly more pronounced. This model facilitates the identification and quantification of various hydrodynamic impacts on flow predictions, including upstream flood propagation, precipitation, and soil moisture conditions. It also elucidates the model's nonlinear relationships and threshold responses, thereby enhancing the interpretability and reliability of streamflow predictions.

The Reception History of The Seven Victories and the Localization of The Seven Victories Spiritual Cultivation

The Seven Victories is one of the most influential works in Catholic literature from the late Ming and early Qing dynasties. The seven victories spiritual cultivation contained therein is the result of the localization of the practice of the Christian faith in the West. It is still a living tradition in the Christian religion and even in Western culture. Since the end of the Ming Dynasty, The Seven Victories has aroused significant repercussions in the ecclesiastical and academic worlds. Some scholars converted to Catholicism because of The Seven Victories and wrote preambles in response to it; some scholars wrote essays criticizing the ethical ideas of The Seven Victories; and some scholars were inspired by The Seven Victories to write about Confucian ideas of sin, the work of reform, and the liturgy of repentance. Together, these constitute the history of the reception of The Seven Victories in China. Through Confucian culture integration, Chinese Christian scholars have developed a localized interpretation of the seven victories spiritual cultivation, resulting in a localized Chinese spiritual cultivation of sin.

International discourses of inclusion and their local translation: Teachers' conceptions regarding an integration-inclusion reform in the canton of Bern (Switzerland)

The inclusive paradigm is an international movement promoted by UN agencies such as UNESCO, influencing the educational policies of certain countries. Switzerland is among the countries that have initiated reforms to make their educational system more inclusive. These reforms have implications for teachers. The objective of this article is to report on teachers' conceptions regarding the ongoing integration-inclusion reform in the canton of Bern, Switzerland. A qualitative methodology was employed, based on 13 interviews conducted and analyzed in light of the theoretical framework of social change. The results highlight that, beyond a principled adherence, teachers' shared conceptions are resistant to integration-inclusion. These conceptions reveal inherent tensions within the inclusive paradigm and its development in educational systems susceptible to other international political influences, such as new public management and neoliberalismo.

SEGAL time series classification — Stable explanations using a generative model and an adaptive weighting method for LIME

Local Interpretability Model-agnostic Explanations (LIME) is a well-known post-hoc technique for explaining black-box models. While very useful, recent research highlights challenges around the explanations generated. In particular, there is a potential lack of stability, where the explanations provided vary over repeated runs of the algorithm, casting doubt on their reliability. This paper investigates the stability of LIME when applied to multivariate time series classification. We demonstrate that the traditional methods for generating neighbours used in LIME carry a high risk of creating ‘fake’ neighbours, which are out-of-distribution in respect to the trained model and far away from the input to be explained. This risk is particularly pronounced for time series data because of their substantial temporal dependencies. We discuss how these out-of-distribution neighbours contribute to unstable explanations. Furthermore, LIME weights neighbours based on user-defined hyperparameters which are problem-dependent and hard to tune. We show how unsuitable hyperparameters can impact the stability of explanations. We propose a two-fold approach to address these issues. First, a generative model is employed to approximate the distribution of the training data set, from which within-distribution samples and thus meaningful neighbours can be created for LIME. Second, an adaptive weighting method is designed in which the hyperparameters are easier to tune than those of the traditional method. Experiments on real-world data sets demonstrate the effectiveness of the proposed method in providing more stable explanations using the LIME framework. In addition, in-depth discussions are provided on the reasons behind these results.

Local Interpretations for Explainable Natural Language Processing: A Survey

As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models. This work investigates various methods to improve the interpretability of deep neural networks for Natural Language Processing (NLP) tasks, including machine translation and sentiment analysis. We provide a comprehensive discussion on the definition of the term interpretability and its various aspects at the beginning of this work. The methods collected and summarised in this survey are only associated with local interpretation and are specifically divided into three categories: (1) interpreting the model’s predictions through related input features; (2) interpreting through natural language explanation; (3) probing the hidden states of models and word representations.

Dual-tracer PET/CT in the management of hepatocellular carcinoma

Background & AimsCombined 18F-fluorodeoxyglucose (FDG) and 11C-acetate (dual-tracer) positron emission tomography-computed tomography (PET-CT) is being increasingly performed for the management of hepatocellular carcinoma (HCC), although its role is not well defined. Therefore, we evaluated its effectiveness in (i) staging, (ii) characterization of indeterminate lesions on conventional imaging, and (iii) detection of HCC in patients with unexplained elevations in serum alpha-fetoprotein (AFP) levels. MethodsWe retrospectively assessed 525 consecutive patients from three tertiary centers between 2014 and 2020. For staging, we recorded new lesion detection rates, changes in the Barcelona Clinic Liver Cancer (BCLC) classification, and treatment allocation due to dual-tracer PET-CT. To characterize indeterminate lesions and unexplained elevation of serum AFP levels, the sensitivity and specificity of dual-tracer PET-CT in diagnosing HCC were evaluated. A Multidisciplinary external review and a cost-benefit analysis of patients for metastatic screening were also performed. ResultsDual-tracer PET/CT identified new lesions in 14.3% of 273 staging patients, resulting in BCLC upstaging in 11.7% and treatment modifications in 7.7%. It upstaged 8.1% of 260 patients undergoing metastatic screening, with an estimated savings of US$495 per patient. It had a sensitivity and specificity of 80.7% (95% CI 71.2-88.6%) and 94.8% (95% CI 90.4-98.6%), respectively, for diagnosing HCC in 201 indeterminate lesions. It detected HCC in 45.1% of 51 patients with unexplained elevations in serum AFP concentrations. External review revealed substantial agreement between local and external image interpretation and patient assessment (n=273, κ=0.822; 95% CI 0.803-0.864). ConclusionsDual-tracer PET/CT provides additional value to conventional imaging in HCC patients by improving staging, confirming HCC diagnosis with high accuracy in patients with indeterminate lesions, and detecting HCC in patients with unexplained elevation of serum AFP.• Impact and implications• Dual-tracer PET-CT detected 12% more disease in HCC patients undergoing staging than CT or MRI, resulting in change in treatment in 8% of cases and a cost saving of US$495 per patient.• It is also able to accurately detect HCC in high-risk cases where CT or MRI are equivocal or normal.• Dual-tracer PET/CT provides additional value to conventional imaging in HCC patients by improving staging, confirming HCC diagnosis with high accuracy in patients with indeterminate lesions, and detecting HCC in patients with unexplained elevation of serum AFP.

Study on English Localization Translation of Domestic Games—Taking the Opera “The Divine Damsel of Devastation” in Genshin Impact as an Example

In recent years, the demand for game enterprises to explore overseas markets has been growing. Not only have more high-quality foreign games been imported into China, but Chinese games have also become an important part of Chinese culture “going out” and “telling good Chinese stories”. This paper takes the global high popularity game Genshin Impact as an example, and combines the Skopos theory to update the research on game localization from the perspective of Chinese-English translation, and analyzes the application of translation and creation in the process of localization translation of drama.

Predicting shear strength in UHPC beams through an innovative neural network with SHAP interpretation

Ultra-High performance concrete (UHPC) has garnered considerable attention in the construction industry due to its exceptional mechanical properties and durability. In the design of UHPC beams, accurately predicting shear strength is crucial. Inspired by the dense blocks in DenseNet, this paper proposes a novel neural network for predicting the shear strength of UHPC beams. A comprehensive database of UHPC beams was initially established through extensive literature data collection, amassing 619 experimental data samples. Preprocessing was subsequently conducted using mahalanobis distance (DM) to eliminate outliers. Subsequently, the database underwent detailed parameter analysis. In practical testing, the novel neural network model exhibited superior accuracy in predicting the shear strength of UHPC beams, surpassing traditional machine learning (ML) models and empirical formulas. Furthermore, it demonstrated excellent generalization capabilities, achieving an R2 value of 0.988 in the training set and 0.964 in the test set. The study introduced the shapley additive explanations (SHAP) method to conduct global and local interpretability analysis of the network model, demonstrating the contributions of each input parameter to the model predictions. Finally, based on this network model, a graphical user interface (GUI) was developed for researchers engaged in UHPC beam design, enabling users to easily input relevant parameters and obtain shear strength predictions. The findings of this study are expected to promote the application of ML models in predicting the complex nonlinear behavior of UHPC structures, providing strong support for engineering practice.

Exploring the Dualism of Law and its Ramification: а Focus on Arctic Subsoil Exploitation

This article analyzes the problem of legal dualism and its ramifications, particularly considering the increased interest of states in exploiting the Arctic subsoil. This article examines the unilateral announcement of the United States government to extend the continental shelf off the coast of Alaska in the Arctic and the Bering Sea, citing provisions from the Russian-American Convention of March 29, 1867, as a prime example of legal dualism. Within this framework, seemingly contradictory concepts are treated as law. The procedural aspects of the treaty that transferred Alaska to the physical administration of the U.S. are analyzed alongside the reasons for the absence of an established border between the U.S. and the Russian Federation. The legal ambiguity surrounding the 1867 treaty, which remains incompletely formalized and thus generates corresponding consequences, is considered. Additionally, this article analyzes actions by the U.S. administration in response to Texas’ claims of secession from the U.S., rooted in differing local and federal interpretations of the U.S. Constitution, as well as the implications of the temporary ban on issuing new export permits for U.S. liquefied natural gas. A solution is proposed to address the adverse effects of legal dualism in this domain by using digital financial assets.

Buddhist Higher Learning Horizon and Its Relation to Global Trends : The Trajectory of Global Buddhist Pedagogy

This study aims to dissect the challenges, identify the unique characteristics, and propose viable strategies for the seamless integration of local and global dimensions in higher Buddhist education. This study used the systematic literature review method with a qualitative approach. The research hinged on an extensive review of scholarly articles, primary texts, and reports on Buddhist education in Indonesia and internationally and then analyzed using thematic analysis. Results highlight that Indonesian Buddhist education possesses unique characteristics and mirrors global patterns, albeit with localized interpretations. The study underscored the importance of recognizing and integrating the Buddhayana spirit amidst global Buddhist sectarian diversities. The contribution of this review was twofold: first, it enriches the understanding of the specificities of Indonesian Buddhist higher education and its relation to global trends; second, it advocates for a harmonious blend of local and global in shaping the trajectory of global Buddhist pedagogy.

FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness data

Accurately predicting patients' risk for specific medical outcomes is paramount for effective healthcare management and personalized medicine. While a substantial body of literature addresses the prediction of diverse medical conditions, existing models predominantly focus on singular outcomes, limiting their scope to one disease at a time. However, clinical reality often entails patients concurrently facing multiple health risks across various medical domains. In response to this gap, our study proposes a novel multi-risk framework adept at simultaneous risk prediction for multiple clinical outcomes, including diabetes, mortality, and hypertension. Leveraging a concise set of features extracted from patients' cardiorespiratory fitness data, our framework minimizes computational complexity while maximizing predictive accuracy. Moreover, we integrate a state-of-the-art instance-based interpretability technique into our framework, providing users with comprehensive explanations for each prediction. These explanations afford medical practitioners invaluable insights into the primary health factors influencing individual predictions, fostering greater trust and utility in the underlying prediction models. Our approach thus stands to significantly enhance healthcare decision-making processes, facilitating more targeted interventions and improving patient outcomes in clinical practice. Our prediction framework utilizes an automated machine learning framework, Auto-Weka, to optimize machine learning models and hyper-parameter configurations for the simultaneous prediction of three medical outcomes: diabetes, mortality, and hypertension. Additionally, we employ a local interpretability technique to elucidate predictions generated by our framework. These explanations manifest visually, highlighting key attributes contributing to each instance's prediction for enhanced interpretability. Using automated machine learning techniques, the models simultaneously predict hypertension, mortality, and diabetes risks, utilizing only nine patient features. They achieved an average AUC of 0.90 ± 0.001 on the hypertension dataset, 0.90 ± 0.002 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset through tenfold cross-validation. Additionally, the models demonstrated strong performance with an average AUC of 0.89 ± 0.001 on the hypertension dataset, 0.90 ± 0.001 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset using bootstrap evaluation with 1000 resamples.

Bridging the Divide: Indigenous Language Integration in Open Educational Resource Accessibility

The use of information resources is dependent on their accessibility by users. The accessibility of information has attracted the attention of researchers from different parts of the world. However, Open Educational Resources (OERs) accessibility issues have not been adequately addressed in South Africa. This article explores the use of indigenous languages in the development and adaptation of OERs for accessibility at the University of South Africa. A total of 108 OERs were available for selection on UnisaIR. Ten of the 108 OERs were purposefully selected. Exploring and analysing OER development at Unisa took a qualitative and exploratory approach. This article presents the findings of a thematic analysis that was used to identify dominant themes in OER. The findings highlight two main concerns that underpin OER operations at Unisa: 1) translation as a method of OER creation and/or adaptation; and 2) deficiency of OER creation in indigenous languages. Though using indigenous languages in the construction of OERs can help to address the accessibility gap in the supply of teaching and learning resources, this aspect of accessibility as a discourse in the OER space has yet to be explored extensively. It is a matter of concern that many OERs at Unisa are written only in English. To address the gap in accessibility of OERs in the South African context, the following three language-related factors should support the creation of OERs: development in local languages, linguistic pluralism, and translation into indigenous languages.

An Interpretable Machine Learning Approach to Predict Survival Outcomes in Spinal and Sacropelvic Chordomas.

Retrospective, population-based cohort study. This study aimed to develop machine learning (ML) models to predict five-year and 10-year mortality in spinal and sacropelvic chordoma patients and integrate them into a web application for enhanced prognostication. Past research has uncovered factors influencing survival in spinal chordoma patients. While identifying individual predictors is important, personalized survival predictions are equally vital. Though prior efforts have resulted in nomograms aiming to serve this purpose, they cannot capture complex interactions within data and rely on statistical assumptions that may not fit real-world data. Adult spinal and sacropelvic chordoma patients were identified from the National Cancer Database. Sociodemographic, clinicopathologic, diagnostic, and treatment-related variables were utilized as predictive features. Five supervised ML algorithms (TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest) were implemented to predict mortality at five and 10 years postdiagnosis. Model performance was primarily evaluated using the area under the receiver operating characteristic (AUROC). SHapley Additive exPlanations (SHAP) values and partial dependence plots provided feature importance and interpretability. The top models were integrated into a web application. From the NCDB, 1206 adult patients diagnosed with histologically confirmed spinal and sacropelvic chordomas were retrieved for the five-year mortality outcome [423 (35.1%) with five-year mortality] and 801 patients for the 10-year mortality outcome [588 (73.4%) with 10-year mortality]. Top-performing models for both of the outcomes were the models created with the CatBoost algorithm. The CatBoost model for five-year mortality predictions displayed a mean AUROC of 0.801, and the CatBoost model predicting 10-year mortality yielded a mean AUROC of 0.814. This study developed ML models that can accurately predict five-year to 10-year survival probabilities in spinal chordoma patients. Integrating these interpretable, personalized prognostic models into a web application provides quantitative survival estimates for a given patient. The local interpretability enables transparency into how predictions are influenced. Further external validation is warranted to support generalizability and clinical utility.

Modelling ductile damage in metals and alloys through Weyl condition exploiting local gauge symmetries

Local translational and scaling symmetries in space–time is exploited for modelling ductile damage in metals and alloys over wide ranges of strain rate and temperature. The invariant energy density corresponding to the ductile deformation is constructed through the gauge invariant curvature tensor by imposing the Weyl like condition. In contrast, the energetics of the plastic deformation is brought in through the gauge compensating field emerged due to local translation and attempted to explore the geometric interpretation of certain internal variables often used in classical viscoplasticity models. Invariance of the energy density under the local action of translation and scaling is preserved through minimally replaced space–time gauge covariant operators. Minimal replacement introduces two non-trivial gauge compensating fields pertaining to local translation and scaling. These are used to describe ductile damage, including plastic flow and micro-crack evolution in the material. A space–time pseudo-Riemannian metric is used to lay out the kinematics in a finite-deformation setting. Recognizing the available insights in classical theories of viscoplasticity, we also establish a correspondence of the gauge compensating field due to spatial translation with Kröner’s multiplicative decomposition of the deformation gradient. Thermodynamically consistent coupling between viscoplasticity and ductile damage is ensured through an appropriate degradation function. Non-ordinary state-based (NOSB) peridynamics (PD) discretization of the model is used for numerical implementation. We conduct simulations of uniaxial deformation to validate the model against available experimental evidence and to assess its predictive features. The model’s viability is tested in reproducing a few experimentally known facts, viz., strain rate locking in the stress–strain response, whose origin is traced to a nonlinear microscopic inertia term arising out of the space–time translation symmetry. Finally, we solved 2D and axisymmetric deformation problems for qualitatively validating the model’s viability. NOSB peridynamics axisymmetric formulation in finite deformation setup is also presented.

Messenger RNA transport on lysosomal vesicles maintains axonal mitochondrial homeostasis and prevents axonal degeneration

In neurons, RNA granules are transported along the axon for local translation away from the soma. Recent studies indicate that some of this transport involves hitchhiking of RNA granules on lysosome-related vesicles. In the present study, we leveraged the ability to prevent transport of these vesicles into the axon by knockout of the lysosome–kinesin adaptor BLOC-one-related complex (BORC) to identify a subset of axonal mRNAs that depend on lysosome-related vesicles for transport. We found that BORC knockout causes depletion of a large group of axonal mRNAs mainly encoding ribosomal and mitochondrial/oxidative phosphorylation proteins. This depletion results in mitochondrial defects and eventually leads to axonal degeneration in human induced pluripotent stem cell (iPSC)-derived and mouse neurons. Pathway analyses of the depleted mRNAs revealed a mechanistic connection of BORC deficiency with common neurodegenerative disorders. These results demonstrate that mRNA transport on lysosome-related vesicles is critical for the maintenance of axonal homeostasis and that its failure causes axonal degeneration.

Chathi Mai in Popular Imagination: Exploring Narratives, Worship, and Rituals in North India

This paper explores the varied narratives surrounding Chathi Mai, which do not find a mention in the normative texts. These narratives showcase the blending of religious beliefs with regional traditions in the imagination of her worshippers. Focusing on Kartika Chatha specifically, the study delves into the narratives that depict the Goddess as a healer, protector, and feminine energy tied to the Sun. Our fieldwork in Varanasi, Patna, Munger, and Sahibganj reveals diverse origin stories, showcasing the cultural mosaic of regions venerating her. The research comprehensively examines the evolutionary trajectory of Chathi Mai’s worship, emphasizing the synthesis of mythological elements and local folklore. Three key facets—the manifestation of feminine energy, the significance of the title “Mai,” and the complementary relationship between male and female principles—are explored to provide a nuanced understanding of the Goddess’s multifaceted identity in North India. The absence of normative texts enhances grassroots adaptability, allowing for localized interpretations in the popular imagination of the Goddess. Associations with the Sun god Surya and the continuity of divine male-female dynamics provide varied entry points for believers, enabling emotional connections and familial devotion. The duality of malevolence and benevolence adds complexity, making worship dynamic and responsive to challenges. Linking Chatthi Mai to Bihar’s regional identity integrates the tradition into daily life, promoting a sense of belonging. The evolving narratives contribute to the deity’s adaptability, relevance, and popularity, with potential for further research exploring folk religions’ adaptation to societal changes and the impact of evolving narratives on community dynamics.

Теоретические подходы к интерпретации локальности рынков

статья посвящена анализу теоретических подходов к понятию признака локальности в исследовании рынков. В работе на основе оценки текстов научных статей, а также практики применения термина локальный, сделан вывод об отсутствии единых подходов к его трактовке. Отмечено, что правовое регулирование также имеет различные интерпретации признака локальности, однако наиболее часто он применяется по отношению к рынку в границах муниципального образования. Установлено, что подходы к определению границ локализации рынка имеют отличающиеся методологические основания и обуславливает целесообразность ведения анализ рынка посредством включения в него расширенного спектра признаков локальности для более глубокого изучения данного феномена. the article is devoted to the analysis of theoretical approaches to the concept of the sign of locality in the study of markets. Based on the evaluation of scientific articles and the practice of applying the term local, the paper concludes that there are no unified approaches to its interpretation. It is noted that legal regulation also has different interpretations of the sign of locality, but it is most often applied to the market within the boundaries of a municipality. It is established that approaches to determining the boundaries of market localization have different methodological bases and makes it expedient to conduct the analysis of the market by including an extended range of signs of locality for a deeper study of this phenomenon.