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Institutional Analysis of Economy: Qualitative and Quantitative Research and Methods

2024· article· en· W4396985659 on OpenAlexaboutno aff
Vitaly Tambovtsev

Bibliographic record

VenueIssues of Economic Theory · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsOperationalizationQualitative researchInterviewQuantitative analysis (chemistry)Task (project management)Management scienceQuantitative researchQualitative analysisQualitative propertyKnowledge managementSociologyComputer scienceEpistemologySocial scienceManagementEconomics

Abstract

fetched live from OpenAlex

The article is devoted to a comparative analysis of two alternative approaches to the study of economic institutions - qualitative and quantitative research methodologies. Analysis of the first shows that the task is to identify the meanings that people attach to their actions, while the task of the second is to identify regularities of connections between institutions and various aspects of behavior. Institutional analysis conducted on the basis of qualitative methodology is based on vague definitions of institutions that lack operationalization, while quantitative studies of institutions rely on strict operational definitions. The article outlines the procedures for both qualitative and quantitative research on institutions, the first of which involves obtaining data primarily through interviewing informants, and the second, primarily through observation of behavior. Accordingly, the result of a qualitative research is metis - local experiential knowledge possessed by members of a certain community, while the result of a quantitative study is a generalized knowledge of regularities expressed by confirmed hypotheses. The obtained comparison results can serve as information for researchers to select a methodology for studying economic institutions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.176
GPT teacher head0.556
Teacher spread0.380 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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