Annexe VII Recommandations de l’Institut National de Santé Publique du Québec (2009) lors du bris d’une ampoule fluo-compacte
Bibliographic record
Abstract
These interviews were initiated and conducted in the early months following the start of the “Special Military Operation” (SMO) in February 2022. Society in Russia had seen the Russian army’s precision weaponry strikes upon the territory of Ukraine, as well as mass migration and partial mobilization, the imposition of several packages of economic sanctions on Russian companies, businessmen and officials, the adoption of laws to combat fake news discrediting the Russian army, and the publication of foreign agent lists. These events led to an initial reaction of shock for Russian society, which is built around the consensus of promised “stability” and values of evolutionary change. These notes contain observations on cultural allusions from in-depth interviews with respondents who were united by their decision not to leave the Russian Federation. By referring to the personalities of the cultural canon, citing, and alluding to literary plots in my description of the present, I was able to identify the order of discourse and establish models for the future, which were not explicitly discussed in the interview but which can be inferred from the discursive strategies used. Four discursive strategies for understanding the present in terms of trauma, loss, apocalypse, and sacred war can provide guidelines for observing Russian society and emerging communities that are reworking the past, grieving, preserving, hoping for renewal, and mobilizing for the future.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.011 |
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".