Bibliographic record
Abstract
Review: Northwest Lands, Northwest Peoples: Readings in Environmental History By Dale D. Goble and Paul W. Hirt (Eds.) Reviewed by Cain Allen Portland State University, USA Goble, Dale D., & Paul W. Hirt (Eds.). Northwest Lands, Northwest Peoples: Readings in Environmental History. Seattle: University of Washington Press, 1999. xiv, 552 pp. ISBN 0-295-97838-4 (paper). US$29.95. Recycled, acid- free paper Anthologies, geographer Yi-fu Tuan noted in his 1974 book Topophilia, have a smorgasbord appeal and threaten us with indigestion should we be rash enough to run through the course. This is true of most anthologies, and legal scholar Dale Goble’s and historian Paul Hirt’s Northwest Lands, Northwest Peoples, a collection of essays on the environmental history of the Pacific Northwest, is no exception. In this case, however, the editors’ choice of essays makes for what turns out to be quite a tasty smorgasbord, while their useful introductory pieces help to minimize the indigestion. Northwest Lands, Northwest Peoples consists of 24 essays divided into six sections, framed by an opening essay by the editors and a concluding essay by journalist William Dietrich. The six section divisions—on place in the Northwest, first peoples, rivers, agriculture, forests, and mining—and their prefaces help to give the book cohesion, as the essays generally complement each other. Part two, for example, consists of five essays on first peoples. The first four essays, written by anthropologists, a zooarchaeologist, and a geoscientist, give the reader a fascinating and comprehensive summary of what we know about the relationship pre- contact Native peoples had with their environment. The final essay, a fine piece by historian Barbara Leibhardt Wester on the 1887 Dawes General Allotment Act’s impact on the Yakama Indian Reservation, is a bit out of place, though, an example of the indigestion anthologies can sometimes trigger. More would have been welcomed on 20th century Indian environmental history. There are similar omissions, great and small, throughout the book. There is, for example, little to no mention of either Canada or the Pacific Ocean in any of the essays, nor is the urban environment given the attention it deserves, an oversight the editors admit in their prefatory essay. The insights a gender analysis of the Northwest’s environmental history would give us are also lacking. These omissions, however, are largely a reflection of the state of the field. There has been little research done on the environmental histories of
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".