A typology of private woodlot owners in Cape Breton, Nova Scotia
Bibliographic record
Abstract
Structural changes in the socioeconomic fabric of rural society in Atlantic Canada and forest ownership in particular have lead to new types of woodlot owners according to their motivations for holding forest properties. These shifts result in increased unpredictability for those in charge of designing policies related to private forest management. In this context, a typology of woodlot owners in Cape Breton, could help inform important questions related to forest policy, for example about how policy instruments can reach these owners and how extension services can address them. In this article we develop an empirically-based typology of woodlot owners in Cape Breton, Nova Scotia, something which has not been looked at so far. Based on data obtained through a mail-survey, we employ cluster analysis to group individual owners into five types. These types differ not only in terms of stated attitudes towards woodlot ownership but also, as revealed by a second-stage analysis based on multinomial logit regression, in terms of observable features of the woodlot and socioeconomic characteristics of the owners. The results will help to forecast future changes in the typology of forest owners since these observable characteristics explain to a large extent ownership type. Furthermore their future behaviors with emphasis on for example keeping, selling, protecting, or harvesting can be predicted to some extent.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".