Career success of Arab women managers: an empirical study in Lebanon
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how women managers in Lebanon account for their career satisfaction and construct their career success. Design/methodology/approach A review of literature along with a survey-based quantitative approach is adopted for understanding the perception of the Lebanese Arab women to their career success. The questionnaire was administered to women participants in managerial and executive roles in different occupational sectors. Findings The findings suggest that despite the glass ceiling that the Lebanese women managers face, they perceived themselves as successful. However, their success was mainly attributed to their satisfaction with the subjective rather than the objective aspects of their careers. Originality/value The value of this paper is three-fold. First, and in view of the Western focus of similar research, this study contributes to the understudied area of research of women managers and their careers in the Arab Middle East. Second, through empirical research stemming from Lebanon, this paper confirms the salience of the glass ceiling in the non-traditional Middle Eastern research locale. Third, it challenges the widespread notion that the subjective and the objective dimensions of career success are correlated. Although the findings cannot be generalized to the entire Middle-Eastern Arab region, they demonstrate important differences in the concept of self-perceived subjective and objective career success.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".