The (Re)Construction of the Organizational Identity of the Canadian Armed Forces Post Institutional Scandal of 2021
Bibliographic record
Abstract
This thesis investigates how military members of the Canadian Armed Forces respond to the sexual misconduct allegations of 2021 among top military officers.The research analyzes how the perceptions of military members and the Canadian military identity have been affected after these institutional scandals.The project builds upon existing literature both in terms of military identity and organizational identity of the CAF and adds to the literature on how the military organization reacts and adapts to sexual misconduct more broadly.The research draws on a social constructionist lens in understanding the opinions, feedback and sensemaking of members as they navigate this unprecedented time.Data was collected from 20 semi-structured interviews with active military members from varying backgrounds, various ranks, and all branches of the CAF including Airforce, Army, and Navy.The study focuses on understanding how members' reactions to, and perceptions of, the scandals has affected their identity focusing on four components: military ethos, affiliation, leadership, and overall CAF reputation.Additionally, the research explores themes of both the dynamic process of gender identity in relation to the organization's identity and the role of the media.The findings highlight that while members do not encourage the unethical behaviour of the allegations or sexual misconduct broadly, there was a clear divide among participants about whether or not the scale of the issue of sexual misconduct constituted a crisis within the CAF organization.Approximately half of the participants note that the behaviour of a few leaders cannot be generalized to all, but the remaining participants argue that there is a clear mistrust within the ranks and that the organization needs to change in order to address these issues.Respondents stated that ultimately whether they agree or not, the organization is shifting due to iii external pressures from the media and from political interference, to become an employer of choice and thus, the organization is embarking on an organizational identity change.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".