Logging firms, nonindustrial private forests, and forest parcelization: evidence of firm specialization and its impact on sustainable timber supply
Bibliographic record
Abstract
Increasing forest parcelization has raised concerns about tract-size economies and sustainable timber supply. We explored this issue by examining the logging sector and forest ownership in northern Wisconsin and Michigan's Upper Peninsula. Using 2004 survey data, we found that 48% of logging firms demonstrated a near exclusive reliance on nonindustrial private forests (NIPFs). NIPF-dependent firms derived 87.5% of their stumpage from this ownership, whereas nondependent firms exhibited a significantly more diversified stumpage supply distributed among public (42.6%), industrial-corporate (33.3%), and NIPF (24.0%) sources. Additionally, NIPF-dependent firms operated on significantly fewer, smaller, and less intensely harvested timber sales, and they were more likely to harvest small tracts profitably. There were no significant differences in the forest products harvested or overall firm profitability. We found statistical evidence that NIPF-dependent and nondependent firms organize themselves differently: NIPF dependency was negatively correlated with total number of employees, timberland area in the firm's wood basket, and firm location and positively correlated with owner age. Results suggest the impacts of parcelization on the logging sector are minimal. NIPF-dependent firms appear to have structured themselves to operate profitably; however, it is unclear how continued parcelization might influence these firms and the sector as a whole.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".