Work and life in the clear‐cut: communities of practice in the northern Ontario tree planting industry
Bibliographic record
Abstract
Based on ethnography, interviews with tree planters and a survey of tree planting contractors, this article focuses on work cultures in northern Ontario tree planting camps. The compressed planting season and the relatively high yearly turnover in the workforce requires that new workers quickly learn how to plant efficiently. These features result in the development of distinctive work cultures and practices that facilitate learning and the sharing of tacit knowledge between planters. Using the concept of communities of practice, we emphasize the social practices that facilitate the integration of planters into their working communities. At one level, tree planters belong to an extensive network of practice and have a shared sense of identity, irrespective of for which contractor, in which region or in which camp, they work. However, at a finer level there are noticeable variations between camps. Both the client for whom planting is done and the operational practices of the tree planting contractor shape the communities of practice in individual camps. However, the most important factor accounting for differences between camps is the process by which communities of practice are socially produced, reproduced and transformed over time and the role, played in this process by worker turnover and retention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".