Bibliographic record
Abstract
Nature sports include pursuits such as paragliding, white-water kayaking, free diving, mountaineering, and surfing. Participants in nature sports interact with geographical features (e.g. mountains, rivers, oceans, snow fields, ice sheets, caves, rock faces) as well as the dynamic forces that produce them (e.g. gravity, waves, thermal currents, flowing water, wind, rain, sun). In this article, I engage a representational approach to analyze how participants in nature sports interact with nature. Anthropocentric representations privilege participants’ interests, wants, desires, and ends; they typically refer to claims of conquest/achievement in nature, or praise nature for its therapeutic qualities. In contradistinction, ecocentric representations recognize humankind as one entity in an interdependent world that comprises all living organisms and the geological processes and geomorphological features that sustain them. Ecocentric representations of nature sports highlight networks of participants and landforms that help preserve a balance between people and the environment. Yet, notwithstanding the allure of ecocentric representations, especially in the wake of evidence that human-induced greenhouse gases are predisposing environmental calamities, there is a substantial gap between the ontological concepts and categories of ecocentrism and lived sporting experiences and practices in nature.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.047 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".