MAPPING THE KNOWLEDGE AREA RESEARCH IN JOB PERFORMANCE DETERMINANTS
Bibliographic record
Abstract
This study conducts a complete review of scientific production using the quantitative approach of bibliometric analysis to help us comprehend the existing structure of studies and to indicate future research directions on job performance determinants and job satisfaction. By restricting its study subjects and recognizing specific trends, the article maps the literature. Descriptive and performance studies were carried out on a sample of 2028 papers using the Web of Science database (WoS). The scientific mapping of the conceptual, intellectual, and social structure was performed using the VOSviewer program, which offers academics with a quantifiable and graphic depiction of the job performance determinants and job satisfaction sector. The results indicated that the field's most notable researchers are of American origin, and the most active nations in the subject of job performance determinants and job satisfaction are China, England, Germany, Canada and the Netherlands, with important partnerships worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.078 | 0.080 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".