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Record W2902461348 · doi:10.5430/bmr.v7n4p22

Gender-Role Stereotypes: Perception of Tunisian Leaders

2018· article· en· W2902461348 on OpenAlexvenueno aff
Hanen Khanche, Karim Ben Kahla

Bibliographic record

VenueBusiness and Management Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsGlass ceilingMeritocracyHierarchyPerceptionAccountabilityIdentification (biology)Occupational segregationContext (archaeology)SociologyPublic relationsPolitical sciencePsychologyLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.343
GPT teacher head0.415
Teacher spread0.071 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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