Re‐thinking<i>tanawwo</i>(diversity) and<i>musawat</i>(equality) in the Middle East
Bibliographic record
Abstract
Purpose The purpose of this paper is to offer an editorial introduction to the special issue on “Diversity management in the Middle East”. Design/methodology/approach The paper reviews the state of extant research on diversity and equal opportunity in the Middle East. It also offers an overview of the papers included in the special issue and the unique contribution they make to this field of knowledge. Findings Findings vary from one paper to the other; however, overall, the papers in the special issue bring to fore alternative types of national and corporate institutions, theoretical ideas, activities and challenges involved in diversity and equality management in the Middle East. Research limitations/implications The papers offer some fresh insights in terms of theorizing and enacting diversity and equal opportunity in departure from the dominant Anglo‐centric theorizations in this field. Practical implications Policy makers as well as business managers may take into account various findings and recommendations offered in the papers in formulating context‐specific policies and practices of diversity management in the Middle East. Originality/value There are not many researchers interested in this field in the Middle East. Thus there are relatively few high quality research papers being produced. Those that are published appear in a wide variety of journals never achieving a critical mass. And the central role of national culture and values is rarely examined. The current (special) issue ofEquality, Diversity and Inclusionaddresses these gaps.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".