Social Consequences of Employee/Management Buyouts: Two Canadian Examples From the Forest Sector*
Bibliographic record
Abstract
Abstract Local control of natural resource processing facilities in small rural communities is often viewed as beneficial to community development. This paper employs social impact assessment tools to examine the social and economic effects of change in the ownership of forest products mills in two communities. Our analysis documents (1) the degree to which local ownership of the new, locally owned corporations led to local reinvestment of profits, and (2) whether the goals of the architects of these buyouts were realized: the maintenance of jobs, income, population, and a way of life. Overall, both communities were able to maintain jobs, population, and real estate values, and profits were reinvested in mill upgrades. After the buyouts, however, both communities experienced a rise and then a decline in community cohesion, and changes in local social and power relations, in which local ownership was short‐lived; benefits to relationships within the community were mixed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".