Making maps is useful but sometimes dangerous : the experience of the Atlas Electronique du Saguenay-Lac-Saint-Jean in Canada
Bibliographic record
Abstract
Il est toujours intéressant d'avoir des réactions provenant des personnes et des organisations à qui s'adressent les cartes et les analyses spatiales effectuées par un groupe de recherche universitaire. Cela est d'autant plus pertinent quand les sujets d'investigation sont proposés non seulement par les chercheurs mais aussi par les personnes qui ont manifesté formellement leurs besoins dès le début de l'opération. Plusieurs thèmes illustrés par l'Atlas électronique du Saguenay-Lac-Saint-Jean au Canada posent de bonnes questions sur le développement local et régional ainsi que sur l'aménagement du territoire. Bien souvent, cela concerne des aspects politiques où les réactions prennent une place insoupçonnée.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.040 | 0.020 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".