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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".