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Record W4409701221 · doi:10.52783/eel.v15i2.2919

Bibliometric Analysis of Global Research on Job Content Plateau

2025· article· en· W4409701221 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Environment
Canadian institutionsnot available
Fundersnot available
KeywordsPlateau (mathematics)Regional scienceGeographyMathematics

Abstract

fetched live from OpenAlex

The participation of Job Content Plateau is gaining popularity in the business and research domains. A thorough bibliometric study was conducted using R Studio to eliminate duplicates and biblioshiny for data visualization and interpretation.. Current study's objective is to perform a comprehensive review of existing research on Job Content Plateau. In order to achieve this goal, bibliometric analysis techniques were used to examine 82 articles about Job Content Plateau that were indexed in the “Web of Science (WoS) and Scopus” between 1989 and 2024. To perform citation, co-citation, co-authorship, and co-occurrence analysis, the biblioshiny program was utilized. The analysis clarified existing research trends and potential directions for future research while identifying the top nations, organizations, writers, journals, and scholarly publications on the subject. The three leading journals in this field of Job Content Plateau are Journal of Vocational Behavior, Journal of Career Development, Group & Organization Management. Tremblay M, Allen T, Jiang Z etc., are very impactful authors. The findings show that the USA is the most productive nation, HEC Montreal is the most productive organization, Tremblay M. is the most productive author, and “The Journal of Vocational Behavior” is the most productive journal. Lastly, the discussion of contributions, limits, and future research objectives concludes.

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: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement 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)0.015
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.062
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.1960.275
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.239
GPT teacher head0.449
Teacher spread0.210 · 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.

Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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