Bibliographic record
Abstract
Physical absences and deployments are a vivid reality for Canadian Forces (CF) members and their families. Whether for training, course work or overseas deployment, CF members can be absent from their families for weeks, or several months at a time as required for military service. My thesis documentary video, Canadian Forces Deployments: A Family Experience provides a glimpse of military families' experiences of deployment of a CF member to Afghanistan. The objective of this video is to provide a representation of the subgroup of military families that differs from the common mainstream media representations of soldiers fighting in Afghanistan who have been or are absent. The basis of this project is ethnographic research, conducted through interviews with spouses of Canadian Forces' members who are either currently deployed in the overseas mission in Afghanistan; who have recently returned; or who are awaiting deployment. This project provides an overview of the military lifestyle of members and their families and the general context for deployments. In comparison to past CF missions, greater concern and risk accompanies current deployments of Canadian Forces members as Canada is engaged in a combat role in the politically unstable country of Afghanistan. Through on-camera interviews with spouses of CF members, this documentary provides a representation that is different than commonly found in mainstream media where military families are often depicted solely in grief and mourning. This project stems from my personal acquaintance with the Canadian Forces and military lifestyle, growing up with my father who was an officer in the regular force. The film is supplemented by this paper, which will develop the theoretical framework and provide a synthesis of the responses of the interviewees.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.037 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".