OD VPRAŠANJ VOJAŠKE KULTURE DO ANALIZE VLOGE ANTROPOLOGOV V VOJSKI – DELO DONNE WINSLOW
Bibliographic record
Abstract
Donna Winslow je bila kanadska antropologinja, ki se je veliko ukvarjala s proučeva- njem strukture in odnosov v sodobnih vojaških silah ter sodobne varnosti. Prispevek predstavlja pregled njenega raziskovanja, ki obsega tudi vrsto znanih in zanimivih del. Tako Donna Winslow razpravlja o posebnosti in sorodnosti vojske preostali družbi, razvoju kanadske vojske ter vplivu globalizacije na konflikte in koncepte varnosti. Posebej zanimiva so njena dela o obredih in kohezivnosti, ki izhajajo iz spremljanja kanadskega padalskega regimenta v Somaliji. Analizirala je tudi projekt Human Terrain in vprašanje uporabe znanosti v vojaške namene. Njena dela kažejo na družbeno angažirano znanstveno delo. Donna Winslow was a Canadian anthropologist who, among other, focused her research interest on the structures and relations in contemporary military forces and contemporary security. In the article, a revision of her most known and interesting works is presented. Discussions are included about specialties of the military and similarities between the military and the rest of the society, about the development of Canadian military and about the impact of globalization on conflicts and security concepts. Among the most interesting things are the works on rituals and cohesion that arise from the inquiring of Canadian Airborne Regiment in Somalia. She also analysed the project Human Terrain and the issue of science in the military use. According to her works, it can be concluded that Donna Winslow was a socially active scientists.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".