Usages de l'information géographique dans la gestion participative du territoire : regards croisés en France, en Belgique et au Québec
Bibliographic record
Abstract
In a societal context influenced by a real politics legitimacy crisis and a declared revival of the local democracy, projects related to land use planning are steadily out in the open in France, Belgium and Quebec as much in the regulation scope as in the practices. Citizens demand more information openness and a right of inspection on decisions running local life. Thus, geographic information as a support for collective reflections remains a real challenge, as it is most of the time in the scope of complex projects. The question of geographic information within the territorial reflection systems, implying involvement time, is de facto begged. This thesis questions in particular the uses of spatial representation (created by geomatic technologies), through the concept of involvement, access and adoption. From a methodological point of view, the realised research puts into perspective, in an original manner with respect to social geography, a Delphi study and explorative case studies, which describe the spatial representation forms used in the involvement process, the nature and the attributes of their users and the involved players motives. The results show the essential nature of a thoughtful and educational use of geographic information within a social learning process. In the same way, they emphasize the indirect use of geographic information technologies for public involvement, which nevertheless are winning public support.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".