A Great Place To Work. How Best Workplaces Affect How Senior Women Perceive Inclusion and Fairness
Bibliographic record
Abstract
In this research, we investigated how senior women perceive working in workplaces that have received the Great Place to Work® label in France, compared to those in other workplaces. Our data came from the anonymous Trust Index© survey of 346,516 respondents from 418 organizations. We used hierarchical linear regression to examine the impact of work in such workplaces on perceptions of inclusion and fairness, as a function of respondent age and gender. Our findings, compared to those reported by Carberry and Meyers (2017) for the United States, suggest that best workplaces may influence these perceptions more strongly in France. While this award serves as a barrier against the sexist double standard of aging, it has a limited effect on how senior women perceive inclusion. Our research contributes to contemporary social exchange theory on intra-organizational social structuration based on age and gender. We suggest that employment branding labels should consider demographic characteristics prior to promoting a workplace as fair and inclusive for all employees, especially in the case of senior women in France.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".