Policy interventions and competing management paradigms shape the long-term distribution of forest harvesting across the landscape
Bibliographic record
Abstract
Industrial economic models of natural resource management often incentivize the sequential harvesting of resources based on profitability, disproportionately targeting the higher-value elements of the environment. In fisheries, this issue is framed as a problem of "fishing down the food chain" when these elements represent different trophic levels or sequential depletion more generally. Harvesting that focuses on high grading the most profitable, productive, and accessible components of environmental gradients is also thought to occur in the forestry sector. Such a paradigm is inconsistent with a stewardship ethic, entrenched in the forestry literature, that seeks to maintain or enhance forest condition over time. We ask 1) how these conflicting paradigms have influenced patterns of forest harvesting over time and 2) whether more recent conservation-oriented policies influenced these historical harvesting patterns. We use detailed harvest data over a 47-y period and aggregated time series data that span over a century on the central coast of British Columbia, Canada to assess temporal changes in how logging is distributed among various classes of site productivity and terrain accessibility, corresponding to timber value. Most of this record shows a distinct trend of harvesting shifting over time to less productive stands, with some evidence of harvesting occurring in increasingly less accessible forests. However, stewardship-oriented policy changes enacted in the mid-1990s appear to have strongly affected these trends. This illustrates both a profit-maximizing tendency to log down the value chain when choices are unconstrained and the potential of policy choices to impose a greater stewardship ethic on harvesting behavior.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".