Downsizing Nature: Managing Risk and Knowledge Economies through Production Subcontracting in the Oregon Logging Sector
Bibliographic record
Abstract
The logging sector in Oregon is characterized by extensive subcontracting between wood-commodity manufacturing firms and independent logging contractors. Why is this so? Considerable recent scholarship has examined the dynamics of flexible production systems, including regional contractor networks, as prominent aspects of late capitalism. Although useful, existing accounts of flexibility are inadequate to explain why logging in particular would be subject to contract production relations. A second literature emphasizes the ‘difference’ of nature-centered sectors, particularly industrial agriculture. I argue that a similar logic applies to logging. That is, natural sources to unpredictable variation and extensive, inconstant geographies restrict the predictability and calculability of production, and the imposition of labor monitoring and discipline. Contracts are a strategy for firms to displace resulting risks and costs onto contractors, while at the same time inducing expert-based rationalization of production. Repeat contracting provides a means of capturing expert knowledge among reliable contractors with knowledge of the parent firm's lands and mills. This is a particularly appealing strategy for vertically and horizontally integrated firms with complex operational portfolios. However, though contracting is one flexibility strategy, Weyerhaeuser's Competitive Logging Program featuring restructured wage relations provides an alternative path to more flexible production, one that further illuminates some of the problems of nature-based production.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".