Citoyennes de la Terre : Portraits de femmes engagées dans la préservation de l'environnement
Bibliographic record
Abstract
Comment s’engager dans des actions en faveur de la vie et du bien commun en cette periode marquee par des problemes d’envergure planetaire tels que le rechauffement climatique, la pollution accrue, l’acidification des oceans ou les menaces sur la biodiversite? Des gouvernements tentent tant bien que mal de s’entendre pour agir. Mais des citoyennes ne les ont pas attendus pour s’engager avec passion et determination dans la preservation de notre milieu de vie collectif et de ses ressources naturelles. Du Tchad a la Russie, du Quebec au Kenya, de la France aux Etats-Unis, des centaines de femmes ont organise, denonce, mobilise, critique, documente, afin que l’humanite prenne conscience de la fragilite de son habitat et de la necessite d’en prendre soin. Politiciennes, scientifiques, avocates, enseignantes, mais aussi designer, mere de famille, secretaire, etc., les femmes presentees dans ce livre ont en commun le sentiment qu’en tant que citoyennes de la Terre, elles doivent agir pour preserver leur planete, au nom du bien commun. Lire le recit de leur vie, c’est s’impregner de leur energie, s’inspirer de leurs luttes et gouter a leur conviction. Une lecture salutaire!
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".