Women managers in the United Arab Emirates: successful careers or what?
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the career success of women in the United Arab Emirates (UAE). The paper examines the interplay of some of the macro-national and meso-organizational factors in explaining the micro-individual experiences of career success. Design/methodology/approach – The paper draws on in-depth interviews with 26 women managers in large private organizations in the UAE to explore whether they experienced their careers as successful or not and the measures they used to operationalize their career success. Findings – The findings presented in the paper support the use of a multi-level research design to capture the complexity of women's experience of career success. The findings illustrate how local cultural values, societal expectations, and organizational attitudinal and structural factors influence the experiences and the conceptualizations of career success of women in this research context. Originality/value – The originality of the paper is threefold. First, the value added of this research lies in exploring whether the women experienced career success or not and the reasons underscoring their experiences, before looking into how they measured that success. Second, the originality of the paper lies in adapting a relational multi-level framework that is commonly used in diversity management studies, to capture the multiple factors that impact the experiences and operationalization of career success of women. Third, the paper contributes to the limited research on the career experience of women in the UAE and the Arab Middle East in general.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| 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".