Outdoor Recreational Activities in France : Comparative Analysis of Territorial Resources
Bibliographic record
Abstract
Outdoor recreational and leisure activities are practiced at a variety of scales throughout much of today’s world. The spatial organization of these practices nevertheless demonstrates very striking geographic disparities. The territory in which outdoor activities take place is far more than a mere framework or an inert support for these activities. It is instead a factor in their coproduction. Thus, the key actors in each territory mobilize the local resources differently. This study is situated within the theoretical framework of the territorial economy, according to which each territory is responsible for finding its own means of development. From this perspective, we analyzed and compared the territorial resources mobilized by the actors in each of France’s 96 continental departments (the department being the territorial unit defined for this research) in their quest to develop an outdoor recreation area. In our typological approach, four variables (further broken down into secondary criteria) were assessed : public sector input, the capacity of the economic structure to support the outdoor recreation sector, the presence of an outdoor culture and appropriate social networks, and natural and environmental resources. Correlations and regularities were observed, which are discussed in terms of the differentiated expansion of these activities in France.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".