Bibliographic record
Abstract
I have known Peter Miller for many years, and have worked with him on several research projects since 1992, including the multidisciplinary Global Integrity Project, which was completed in 1999. One of the purposes of that project was to investigate the commonly held view that environmental protection and human welfare are in conflict. The goal was to demonstrate that the MconflictM is more apparent than real, at least at the level of health and life. Each conflict that pits human interests against ecological considerations is too facile ifit does not understand the intimate connection between natural processes and all of life, including that of human kind. More specifically, there is no conflict between the preservation of certain core or wild areas (Noss, 1992) and human welfare, because these areas must be maintained not only to support life within their confines, but also for the benefit of all life beyond their borders. Miller's work on the project was concerned with forestry policy, as he has worked extensively in the area of sustainable forest management, combining both theoretical interests and practical experience as a committed environmental activist. In this paper I discuss some of the issues Miller addresses in an article co-authored with James Ehnes as part of the Global Integrity Project (2000). Miller and Ehnes begin by summarizing what appear to be conflicting values concerning forests and forestry in Canada:
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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 | 0.003 |
| 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 | 0.020 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".