Public Leadership for the Inclusive Workplace – An Empirical Study in France
Bibliographic record
Abstract
About 15% of the world’s population (around 1,3 billion people) live with disabilities (WHO, 2022). This fact coupled with the difficulty that organisations have in finding talent (see Lazarova, Thomas & Farndale, 2021) makes it obvious that organisations need to find ways to leverage the skills and talent these individuals can bring to the workforce. Despite legislation for disabilities integration into the workplace in various countries, it appears that public sector employment has better awareness and conscious implementations for disabilities’ integration in the everyday workplace. This may even be a general feature across quite a large number of countries, since those administrations stand for example, societal advances and the more inclusive workplace without being too much under competition from ‘free markets’. Hence public sector organisations seem to have an unrecognized leadership in integrating special needs’ employees with most often far better provisions and professional, inclusive workplace design to adapt it to several kinds of disabilities with higher integration of affected employees/civil servants. Even contractual employees with public sector or public missions may have more opportunities since the public mission involves longevity of actions for most purposes. We study the empirical case of France to corroborate such inclusive workplace leadership with the particular focus on public sector employment.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".