Bibliographic record
Abstract
Until the development of the chainsaw and the intensive mechanization of the lumber industry during World War II, rank-and-file workers dominated the labour force in the woods and mills of the North Pacific slope and the American South. Those workers produced good profits for the owners of capital and were able in many instances to extract a modest living for themselves. From the histories that have been written about the turbulent life in many timber communities, it is clear that workers were not passive in the face of arbitrary decisions made by logging and sawmill bosses. Those who worked in the woods and mills of British Columbia, the American Northwest, and the great pineries of the American South struggled – often against great odds and under demanding circumstances – to gain a fair share of the wealth they produced. While mill owners, resident managers, and their allies in the business community usually controlled local politics, I argued in Hard Times in Paradise: Coos Bay, Oregon (2006) that their working-class constituents frequently tempered decisions and helped forge a political culture that embraced some of the hopes and aspirations of common people. Although the industries and work life covered in these three books span different corners of the North American continent and different moments in time, there are interesting parallels between industrial life in the towns and woods of these disparate regions. From British Columbia’s lumber towns to REVIEW ESSAY / NOTE CRITIQUE
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.018 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".