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Record W7035502663

The achievements and challenges of women in combat: a comparative study of Israel and Canada

2023· dissertation· en· W7035502663 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicMedical, Sociocultural, and Biopolitical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFace (sociological concept)DemocracyDutyMilitary theoryMilitary policyMilitary psychologyGovernment (linguistics)Military strategy
DOInot available

Abstract

fetched live from OpenAlex

In 1993, the recognized Oxford scholar John Keagan made the following assertation in his book A History of Warfare: “Women, however, do not fight. They rarely fight among themselves and they never, in any military sense, fight men. If warfare is as old as history and as universal mankind… it is an entirely masculine activity”. Almost 30 years have passed after combat roles were opened to women in at least 25 military institutions around the world and still, the mere presence of women in combat duty keeps being challenged. This investigation asks, what achievements and challenges do women face while serving in military combat and combat-support positions? This research is expected to contribute to the studies of women’s representation, recognition, and visibility in warfare, defense forces, and strategic studies with the analysis of women’s integration into military combat units. My research fills a void in Feminist IR theory inclined to view the women combatants based on the barriers and abuses they have experienced while serving in the military. I demonstrate and explore women’s unsuccessful and successful incorporation into the armed forces of two states: Israel and Canada - democratic industrialized nations that have enforced defence policy shifts to actively incorporate women into combat roles as earlier as 2000-2001.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.275
Teacher spread0.229 · 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 teacher head, not a consensus.

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

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

Citations0
Published2023
Admission routes1
Has abstractyes

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