1 Forthcoming in Environmental Politics From Wilderness to WildCountry: The power of language in
Bibliographic record
Abstract
A wild language Love the Wild? Help keep it Wild (John Muir Trust flyer, 2006) Wild people (colonisers) make wild country (degrading, failing) (Rose, 2004 p.4) Environmental campaigns worldwide are often framed as conserving wilderness and preventing exploitation of natural resources. Organisations lobbying for protection of vast tracts of Alaska, Canada, and Australia employ an emotive language using words such as pristine, untouched, undisturbed, intact expanse and wild frontiers to rally support. Photographs of lands empty of people or any human structures often accompany these words, see Figure 1. This approach has proved successful in arguing for the need to place legal boundaries around tracts of land through World Heritage Status or as National Parks. Figure 1: The Wilderness Societys Wild Country logo Despite this, there is increasingly recognition not only that indigenous people have historic rights of possession to some of that land, but also that indigenous environmental knowledge and land management practices can be beneficial for conservation outcomes. This recognition and the legal changes that have accompanied it (such as the development of Native Title in
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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 | 0.011 | 0.016 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".