Behind the veil: an exploratory study of the myths and realities of women in the Iranian workforce
Bibliographic record
Abstract
Despite significant advancements, women executives and professionals have continued to complain about the existence of a glass ceiling in the UK, the USA, Canada and other countries that espouse equal employment opportunities. Little is known about the role and plight of women in the workforce in Islamic countries. In general, Western societies perceive the participation of women in the workforce and their upward career mobility as very limited in Islamic societies. This article seeks to address this limitation in the field by unravelling some of the myths and realities pertaining to women in the Iranian labour force. Based on interviews with 12 Iranians, reality checks were performed on five widely held perceptions of the status of women in Iran, including women's participation in the labour force, appointment to managerial/professional positions, representation in higher education, segregation in the workplace, and Islam's attitude towards non‐Muslims. These issues are discussed in the contexts of foreign direct investment in Iran and other Islamic countries. Implications for international human resource management, including the assignment of women expatriates to Iran and other Islamic nations, are also addressed.
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 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".