Gender-Role Stereotypes: Perception of Tunisian Leaders
Bibliographic record
Abstract
The objective of this paper is to report on the problem of the glass ceiling in Tunisian companies. First, by recalling on the basis of statistical findings the situation of women at work, and then highlighting the main results of the surveys carried out in Tunisia on the question, and finally by highlighting some prospects for the strategies envisaged to go beyond The glass ceiling.While organizations are places of meritocratic recognition in which more and more women graduate into skilled occupations, they are also places where informal, often unequal power relationships are built that determine access to decision-making positions. Women are becoming increasingly scarce as they rise in the hierarchy and remain a minority in high-level decision-making and accountability positions. They have less access to hierarchical positions (Ben Hassine, 2007). They are often limited to administrative or relational activities (Gadéa, 2003). Thus, in the private sector, out of 30 large Tunisian companies, only 4 of them have a woman on their works council (GIZ, 2013).The identification of the different factors involved in the glass ceiling also raises questions about the behaviors and strategies developed in the context of organizational contexts reproducing the male career model, as well as the diversity of these behaviors. This study also allows us to consider changes and strategies of change in career development and women's access to decision-making positions that will push the boundaries.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".