Meritocratic Organizational Behavior Profile Informetry: Mapping the Evolution of the Scientific Context Between 2010 and 2020
Bibliographic record
Abstract
Research has presented that organizational behavior derives from some factors that may have got the potential for positive and negative effects on people's performance in organizations. The objective of the development of this study was to perform a systematic mapping of the evolution of the literature regarding the meritocratic organizational behavior construct, aiming to organize, evaluate and integrate scientific evidence with the selected articles. In this study of mapping the specialized literature, the Web of Science Scopus databases were used, through the application of informetric techniques using the bibliometrix package of the R language. The mapping of the evolution of behavior at work and merit in the organizational context made it necessary to contextualize the empirical research that supports the proposal of this study. The descriptive results illustrate the evolution of publications involving the terms “organizational behavior and merit or meritocracy” over the period studied, which was confirmed by the verification of the average annual growth rate of publications of 10.84%. An important demonstration refers to the clear identification of the capacity to produce publications by country. The mapping of time tracks related to publications on organizational behavior and merit presented that the United States of America is the major protagonist of this current of research at a global level. It was observed that a combination of aspects that involve not only the management of organizational behavior and its relationship with meritocracy between genders and diversity at work, as well as the merit in careers and promotions, and, mainly, the meritocratic system for performance as the focus of public management.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.029 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".