Bibliographic record
Abstract
Ce Hors-série de [VertigO], intitulé Patrimonialiser la nature, fait suite à un colloque international organisé par le Laboratoire Société, environnement, territoire (SET), à l’Université de Pau et des Pays de l’Adour, du 6 au 8 septembre 2011. Sont regroupés ici quelques articles issus de la soixantaine de communications présentées par des chercheurs de diverses disciplines des sciences humaines et sociales. Les articles de ce hors-série de [VertigO] explorent le processus de patrimonialisation de la nature dans sa composante géographique. Si certains aspects du patrimoine naturel ont en effet été particulièrement étudiés (aires protégées, gestion, mise en valeur touristique, etc.), la compréhension du processus d’appropriation, de sélection d’objets ou de lieux désignés comme patrimoine ainsi que ses effets sur l’espace, restait davantage à explorer. Le colloque a été soutenu par le Conseil régional d’Aquitaine, le Conseil général des Pyrénées-Atlantiques et la Communauté d’agglomération Pau-Pyrénées.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".