Bibliographic record
Abstract
Review: Balancing Act: Environmental Issues in Forestry (2nd ed.) By J. P. Kimmins Reviewed by Joseph J. Nocera Nova Scotia Department of Natural Resources, Canada Kimmins, J. P. Balancing Act: Environmental Issues in Forestry. (2nd ed.) Vancouver, Canada: UBC Press, 1997. 305 pp. ISBN 0-7748-0574-9 (softcover). Finding a balance in a debate of extremes is a daunting task. In Balancing Act, the author attempts to describe all points between aggressive environmental protectionism and outright resource exploitation. Throughout the book, the reader is presented with numerous facts, theories, and research concerning the effects of exploitation and the fallacy of eternal hands-off protectionism. His facts are precise, and his presentation of all aspects of forestry is thorough—from climate change to groundwater issues. J. P. ( Hamish ) Kimmins offers a complete and extensive primer on forestry, forest science, and relevant topics. This background consists of five full chapters (73 pp.) and provides, as the author states, the average concerned citizen or forester with an introduction to the ecological aspects of the major environmental issues facing the managers of Canada’s great forest resource (p. 4). Even those readers armed with intricate knowledge of these disciplines will take home something new, or at least be reminded of something they may have forgotten. This lengthy introduction is well written and is the greatest strength of this book. Has the author managed to find a balance, as the title suggests? Throughout, Kimmins disclaims this work as not being a vehicle meant to resolve a debate. I suggest that he has accomplished neither the intended, or unintended, objectives. The entire work is one of a pro-forestry nature, albeit against blatant exploitation, where forests are considered a resource (e.g. see quote above). The objectivity that the author desires is lost in his repeated advocating of active forestry over forest protection. This is most evident in the very limited discussion on some sensitive wildlife issues. Wildlife that are dependent upon old-growth (requiring the least intensive style of forest management), seemingly receive a brush-off, with only one paragraph devoted to their discussion (p. 150). The reader is told how harvested forests beget different suites of species, but that overall diversity is unharmed. What about the inherent value of wildlife that resided there? Many of the issues raised and information presented are generated from the west coast of Canada, particularly the author’s home province of British
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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".