La collectivité apprenante : une stratégie de développement local
Bibliographic record
Abstract
The phenomenon of connected communities shows that the Internet can be a powerful tool in promoting cohesiveness between community actors. Indeed, public and nonpublic initiatives with the aim of networking members of a community through a virtual platform, and attempts to federate existing local initiatives through a collective portal are multiplying in the developed countries. This has given rise to expressions such as"connected city,"intelligent city," and"digital city." However, uses developed from Internet applications have remained primarily instruments limited to information dissemination or service delivery. In the thesis, we maintain that it is possible to go beyond the instrumental character of Internet applications and to give a developmental character to processing for designing and developing a collective portal. A learning community is a completed form of connected community that promotes local actors to develop a creative synergy that can yield ideas, collaboration, and development projects. In addition to promoting the use of ICTs, a learning community project can stimulate public participation in community activities, redefine community governance, and give rise to a relational strategy that can generate the knowledge, distinctive competences, and collective capabilities that influence the direction of community development.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".