Who joins the Canadian Forces? : developing a framework for analysis using Bourdieu, Habermas and Giddens
Bibliographic record
Abstract
This thesis presents the results of an exploratory study aimed at developing an understanding of Canadian Forces demographics and linking those demographics to current bodies of sociological theory. The background and literature review provide a starting point for sociological analysis; the study begins with a detailed review of existing literature in Canadian and United States military sociology, utilizing an exploratory approach that incorporates key elements from Bourdieu’s concepts of field and habitus, Habermas’s lifeworld and structure, and Giddens’s notion of structuration. Once the key sociological theories are isolated, research methods and methodologies are developed. Data are collected from the 2006 Canada Census and the demographics of Canadian Forces members are explored through a logistic regression model. Data are interpreted within a sociological framework based on an integration of select theories from Bourdieu, Habermas and Giddens. This research identifies weak relationships between demographic characteristics and CF membership, along with socioeconomic factors and Canadian Forces membership. The completed research provides a starting point for future analyses in Canadian military sociology. Given that demographic and socioeconomic factors demonstrate weak correlation with Canadian Forces membership, future studies can focus on the motivations of Canadian Forces members knowing that background characteristics do not predetermine service. Although the Canadian Forces is primarily composed of Caucasian males, this accounts for only a small portion of variance in the Canadian Forces membership variable.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.018 | 0.028 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".