Bibliographic record
Abstract
This article examines the origins, evolution, ideology, and political impact of an environmental coalition in the 1970s. Two wilderness activists in northwestern Ontario challenged established preservationist groups to shift their advocacy from public battles over management policy for individual parks, to design and promote a system of provincially-owned wilderness parks. To build public support and maximize their political clout, the two advocates persuaded five groups to form the Coalition For Wilderness (CFW) in 1973. Unfortunately CFW was mostly a two-man show. Constituent groups gave insufficient material support because of their diverse interests, economic woes, and the “free rider” problem. Nevertheless, CFW’s tactic of privately lobbying park planners within the Ontario Ministry of Natural Resources had some political impact. It generated policy information and educated the public about the need for a wilderness park system, thereby supporting the parallel efforts of the bureaucrats. Ironically, the coalition’s scientific rationale for protecting wilderness limited its influence among planners and the wider advocacy community, both of whom regarded recreational and other reasons for wilderness protection as more politically defensible than science. This difficult episode taught the CFW leadership valuable lessons, enabling Ontario preservationists to build more successful coalitions in the 1980s and 1990s.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.032 | 0.151 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".