De la ressource à la trajectoire : quelles stratégies de développement territorial ?
Bibliographic record
Abstract
Dans la continuit de prcdents travaux, cet article s'intresse la notion de ressource territoriale en portant plus prcisment sur le rle de l'oprateur des ressources. Pour cela, il propose d'tudier les dynamiques territoriales l'aide d'une matrice des trajectoires de construction et de valorisation des ressources. Cette matrice constitue par la suite une grille de lecture indite de l'oprateur des ressources. Cette mthode, avant tout qualitative, permet d'identifier l'oprateur et d'analyser ses 1 Cet article s'inscrit dans la continuit de travaux mens sur la ressource territoriale depuis plusieurs annes. Il fait notamment suite un prcdent article publi dans la RERU Dans la continuit de cet article, les auteurs ont eu l'occasion d'organiser un atelier Ressource territoriale : objets et mthode lors du XLIII e colloque de l'ASRDLF Grenoble-Chambry durant lequel la question de l'oprateur avait t souleve et avait fait l'objet de nombreuses discussions (une version rvise de la communication prsente cette occasion a t publie sous la rfrence Dans la continuit de ces dbats, les auteurs ont prsent une communication lors du XLV e colloque de l'ASRDLF Rimouski. Le prsent article est directement issu de cette communication et des discussions au sein de l'atelier Les ressources naturelles et culturelles et leurs liens aux territoires et l'environnement .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".