Apprentissage Social et Mobilisation Citoyenne pour une Gouvernance Démocratique et Durable de l’Eau au Mexique
Bibliographic record
Abstract
Resume: En 2012, la Constitution mexicaine a reconnu le droit humain a l’eau et il a ete decide d’elaborer une loi generale des eaux (Ley General de Aguas, LGA) pour garantir ce droit. A ce moment-la, les partis politiques et le gouvernement federal se sont mis au travail, en meme temps qu’on assistait a un phenomene presque inedit : un large eventail d’acteurs –des scientifiques, des professionnels du secteur associatif, des cadres d’organisations de base, et autres– se sont organises pour entreprendre un processus d’apprentissage collectif, d’echange de savoirs et de redaction d’une proposition citoyenne de LGA. Les enjeux (en termes de qualite de vie, de tissu social et d’environnement) sont importants : des visions politiques et epistemiques tout a fait differentes se font face : d’un cote, celles du gouvernement federal et de la plupart des legislateurs; de l’autre, celles des citoyens organises. Cet article analyse les processus de mise en reseau et d’hybridation de savoirs et de pouvoirs dans cette mobilisation sociale, afin d’en tirer des apprentissages qui puissent contribuer a l’action et a la reflexion des acteurs investis : les communautes, les organisations et les institutions de recherche. -- Abstract: In February 2012, the Mexican Constitution recognized the human right to water, and a delay was fixed for a new Ley General de Aguas (LGA) to be presented to Congress. While political parties and the federal government got to work, other social actors did the same; scientists, professionals from NGOs, and some grassroots organizations began to meet, exchange their knowledge and coordinate efforts at a national level to draft out a citizen proposal for a LGA, an initiative almost unseen previously in Mexico. Many important issues are at stake, in terms of life quality, social fabric, and environment; a sharp contrast in terms of political and epistemological positions is evident between the federal government and most members of congress, and that of organized citizens. In this article the author analyzes the processes by which networks are created, allowing knowledge to flow and giving birth to new forms of social learning and of political organization between a diversity of actors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".