Perceptions of organizational injustice in French business schools
Bibliographic record
Abstract
Whereas the institutional drivers of the accountability discourse and the apparatus of performance evaluation accompanying such a discourse in the neoliberal university are well documented, their implications at the individual level have received lesser interest. Our paper suggests that more attention be paid to the voices and the experiences of the “governed”. It accounts of the unfairness of the accountability regime in higher education, and more specifically in business schools, as it is perceived by scholars in France. Using insights from the institutional complexity (IC) and organizational justice (OJ) literatures, as well as an empirical analysis of the French business scholars’take on their changing work context and the metrics against which their performance is assessed, our study extends the understanding of the implications of organizations’ rewards, incentives, performance control and evaluation practices for OJ. Moreover, it deconstructs the narrative of the accountability regime by reminding that institutional complexity leaves very little room for many scholars to be star researchers, excellent program managers, innovative and inclusive pedagogues as well as impactful public servants at the same time without hindering other academic missions they value (disinterested collegiality, care, social inclusion), their quality of life, family, and or health.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".