Bibliographic record
Abstract
Finland has struggled to formulate and implement policies for the national grey wolf ( Canis lupus ) population. Institutional adjustments were undertaken to improve wolf protection and human–wolf coexistence, but the wolf population has decreased. This calls for an explanation. I will apply Charles S. Peirce’s concept of habits and his semiotic theory to understand why it so difficult to design and implement a workable wolf policy. I intertwine Peircean methodology with the ontology provided by ecological economics and the analytic epistemic tools by old (traditional) institutional economics. Institutions exist to serve human purposes, and the modification of institutional infrastructure affects how social-ecological functions can still bring absent features of policy and management into existence. I therefore explicate the semiotic interplay of policy signs, objects, and interpretants in wolf management adjustments and consequent outcomes. Finally, the difficulty of habit formation for coexistence will be discussed and policy advice given.
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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 | 0.000 | 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) | 0.000 | 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".