CO-CONSTRUCTING PUBLIC POLICY FOR THE SOCIAL AND SOLIDARITY ECONOMY
Bibliographic record
Abstract
Ce numero d’ Universitas Forum contient des versions abregees des documents de references et des etudes de cas par pays prepares pour le Forum International de l’Economie Sociale et Solidaire (FIESS), qui s’est tenu a Montreal (Canada) en Octobre 2011. L’objectif du FIESS etait sur les politique publiques et plus specifiquement sur la coconstruction de celles-ci: ou est ce que c’etait necessaire? dans quels pays; dans quels secteurs; quels sont les processus de co-construction dans les differents contextes nationaux? L’objectif des documents de travail, rediges par des experts sur chacun des cinq themes du FIESS, etait de fournir un apercu sur les defis et les enjeux souleves par chacun d’entre eux (territoire et developpement local; innovation et entreprenariat collectif; finance solidaire, travail et emploi, securite alimentaire et souverainete) et sur les relations entre gouvernements et societe civile dans plusieurs pays, qui sont des illustrations utiles d’approches de collaboration a la formation politique. Alors que l’economie sociale et solidaire continue d’evoluer, ces documents sont offerts en tant que “work in progress”. Les documents de travail complets sont disponible ici: http://reliess.org/centre-de-documentation/
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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".