Integrating gender and cultural perspectives in Canada’s Professional Military Education: transforming military culture through informed leadership
Notice bibliographique
Résumé
This dissertation investigates and invests in the possibility for feminist transformational change within militaries as well as the potential for militaries to be 'forces for good.' The research considers whether militaries can contribute to feminist progress and work towards the cultural inclusion of diverse members within militaries due to personnel's exposure to gender and cultural perspectives within Professional Military Education.The project narrows its investigation to the mid-to-senior graduate level education of Canadian military officers within the Joint Command and Staff Programme at Canadian Forces College.It applies post-modern feminist, intersectional and militarized masculinities theories to understand the military learning environment and to analyze the inclusion and reception of critical theory by military learners.The research draws on contemporary pedagogic literature to make recommendations for optimizing learning environments and professional competencies to facilitate inclusive security and organizational culture change.Acknowledging the context of dominant masculinist and white centering constructions of military identity and socialization, this investigation asks: To what extent are gender and cultural perspectives integrated into mid-to-senior level Canadian Professional Military Education?If and in what ways military socialization and culture shapes the learning environment and the reception of this education?Finally, if and in what ways such learning has facilitated feminist transformations in the military and beyond?The research draws from a feminist critical discourse analysis of six semi-structured focus groups across military and civilian educators, curriculum developers, librarians, and students as well as eight in-depth interviews with military students before graduation and eight follow-up in-depth For Papa.I'll be loving you always.I wrote this dissertation in my home office in Toronto, situated on the traditional territory of many Indigenous nations including the Mississaugas of the Credit, the Anishnabeg, the Chippewa, the Haudenosaunee and the Wendat peoples.My place of work and residence falls under Treaty 13 referred to as the Toronto Purchase negotiated between the Mississaugas of the Credit and the Crown.Indigenous peoples of this land are its longstanding guardians.As a person of settler colonial heritage, I benefit from the land, its communities of people, and their knowledge.I acknowledge these privileges and am committed to supporting the ongoing stewardship of Turtle Island and Toronto by diverse First Nations, Inuit, and Metis peoples.I dedicate this dissertation to my late father, John Brown.My Papa taught me to learn from the perspectives of others, to listen, and to never, never, ever, give up.This project was possible due to the love and support of my father and so many people, especially my family.To my late stepfather Gerry Wapnah, I love you and miss you every day.To my mother, Katherine Brown, thank you for your love.Thank you also for your belief in me and for reminding me that this project is about changing the world for the better, one step at a time.I am thankful and blessed to have another
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,036 | 0,018 |
| Communication savante | 0,012 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,003 |
| 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».