Logging across Borders and Cultures: An Example in Northern Maine
Bibliographic record
Abstract
Persistent concerns about the continued use of foreign labor and the viability of northern Maine's logging industry prompted further research on the cross-cultural logging workforce found in Maine's counties that border the province of Quebec. Two distinct populations of woods workers are employed in these border counties: Maine residents and Quebec residents. This study examined sociodemographic attributes, sense of occupational choice and prestige, and familial attachment held by these two populations of loggers, as well as barriers to business expansion felt by logging entrepreneurs. Significant differences in age, education, logging experience, attitudes toward logging, and perceptions of public image were found between Maine and Quebecois loggers. Additionally, despite an intergenerational labor supply that historically characterizes the logging industry, more than 50 percent of loggers from both countries would not encourage their children to enter the logging profession. These factors may not only pose challenges for logging business stability and labor recruitment efforts in this region but also impact the economic vitality of the forest products industry as a whole. Furthermore, the findings from this research may be of interest and pertinent to those engaged in forest products industries within other cross-border regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".