Tangled root: The paradoxical development of British Columbia's tree planting industry.
Bibliographic record
Abstract
British Columbian tree planters have cultivated a reputation for being countercultural mavericks within the world of forestry. While this standing is well earned in many respects, it also implies that the job is ethically structured in a way that somehow opposes industrial resource extraction like clear-cut logging. Contrary to these perceptions, this study will show how throughout the history of tree planting in B.C. reforestation workers have been given incentive to fulfill the forest industry's mandate to plant trees faster and better, thereby acting as critical participants in maintaining the legitimacy of the modern forest industry. I will show that despite their reputations or personal values, tree planters in British Columbia have also existed - both philosophically and practically - in a symbiotic relationship with intensive harvesting practices. Far from solely being an idealistic social experiment, this prototypical model of a sustainable industry became successful through the displacement of the distinction between binaries like green and corporate, counterculture and capitalist, as well as tree-huggers and loggers. As a result, it is common for contemporary reforestation workers to exude an occupational culture that is bohemian but also maintains a widespread emphasis upon ultra-efficiency, competition, and money-making - traits that have generally intensified over time. --P. ii.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".