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Record W4372348940 · doi:10.18280/ijsdp.180423

Identifying the Absence of Knowledge Using Bibliometric Analysis: The Case of the “Bad Behavior Index”

2023· article· en· W4372348940 on OpenAlexvenueno aff
Mohammad Tariq Al Fozaie

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsIndex (typography)StatisticsEconometricsComputer scienceMathematics

Abstract

fetched live from OpenAlex

There are various hypotheses as to why some nations are more developed than others, including political instability, corruption, and exclusive institutions.However, one hypothesis that has not been extensively studied is the impact of 'bad behavior' on development.To test the validity of such hypothesis, a composite index must be developed to quantify such behavior, and facilitate for the study of its impact on development.This study aims to lay the foundation for the development of such index by conducting a bibliometric analysis of Scopus and Web of Science databases to determine if a quantitative measure of this concept already exists.This study employs a bibliometric and content analysis of these databases using Excel, VOSviewer, and R software.The results of such analysis indicate the absence of such a composite measure, thereby providing sufficient evidence that the construction of a 'Bad Behavior Index' is justified.Regarding the research contribution, the study offers an improved methodology for conducting bibliometric analysis by integrating and analyzing two journal databases instead of one, and using three different mediums and methods to identify the absence of knowledge, and provide a justification for the creation of such knowledge.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.099
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0680.071
Science and technology studies0.0020.003
Scholarly communication0.0090.007
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.376
Teacher spread0.303 · 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

Labeled directly by 2 models reading the full record.

Bibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
Domainnot available
GenreEmpirical · Review

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

Citations6
Published2023
Admission routes1
Has abstractyes

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Same venueInternational Journal of Sustainable Development and PlanningSame topicCorruption and Economic DevelopmentCategoryBibliometricsFrench-language works237,207