Government supported women’s leadership development programmes: a case study of Dubai.
Notice bibliographique
Résumé
Since its inception in 1971, the United Arab Emirates’ (UAE) government has undertaken to develop the capabilities of its citizens (nationals), in order to satisfy the country’s need for rapid economic growth. One of the seven Emirates, Dubai, has played an integral role in implementing this national objective by leading associated initiatives. Consequently, the government of Dubai emphasizes the importance of developing its people through human resources programmes that focus on specific demographic groups, such as women, and specific aspects of human development, with leadership being an important segment. The purpose of this study is to examine women’s leadership in the UAE. Specifically, this study attempts to explore the characteristics and behaviours of a group of women leaders who were the first candidates of the UAE Women Leadership Development Programme (UAE WLDP) in Dubai. \nThe study included an on-line survey that was sent to a sample of 35 women leaders. Demographically, all of the respondents were aged between 25 and 35 years. Most of the women were unmarried and held degrees of higher education, specifically from a business background. The survey indicated that respondents believed they practiced a combination of the four categories of leaders’ behaviours: pragmatist, visionary, motivator, and facilitator. Finally, they rated their managerial potential and management performance as very good. \nThe respondents reported that one of the major challenges they faced at work was gender discrimination and issues related to work-family balance. A quarter of the women indicated that they felt some kind of gender-bias towards them. Respondents suggested policies or interventions that could be undertaken in order to improve their performance as leaders. \nThe study concluded that government sponsored leadership programmes enhance the abilities of women who were already progressing in their chosen fields. It highlights other areas in which the government could intervene to improve the capabilities of women in leadership. Overall, although good progress has been made, more could be done to benefit the national population using programmes of this nature.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».