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Record W4206456041 · doi:10.22215/etd/2021-14630

Integrating gender and cultural perspectives in Canada’s Professional Military Education: transforming military culture through informed leadership

2021· dissertation· en· W4206456041 on OpenAlexafffundabout
Vanessa Brown

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsCarleton University
FundersCanadian Armed Forces
KeywordsSocializationInclusion (mineral)CurriculumContext (archaeology)PedagogySociologyPolitical scienceIdentity (music)Transformational leadershipPublic relationsGender studiesSocial science

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.146
Threshold uncertainty score0.991

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0360.018
Scholarly communication0.0120.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.071
GPT teacher head0.354
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreOther

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".

Quick stats

Citations6
Published2021
Admission routes3
Has abstractyes

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