RETRACTED: The strategic impact of performance appraisal on corporate governance dynamics
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the strategic impacts of performance appraisal on the corporate governance dynamics (in the private sector) in addition to the advanced assumptions which traditionally explain the declining tendency related to the board of governance's evaluation. Design/methodology/approach A quantitative analysis by regression approach was mobilized for the needs of this technical and conceptual paper. In order to proceed, the author use a model of workforce determination which examines the influence of appointments, the termination of functions, the solid mechanisms of evaluation and strategies of control. Findings The results' degree proved itself to be slightly higher and consequently, the influence of management evaluation was significant. As expected, the planning mainly reflected, in a unique way, a large and growing proportion of new appointments. Originality/value The objective of the paper is to validate the impacts of business management evaluation on the dynamics of the governance workforce in the private sector. This has never been done in any empirical study before. The approach allows to present an original conception of the influence model of evaluation and the planning evaluation relationship factors.
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 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 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".