Bibliographic record
Abstract
Ecosystem management, which is premised on the goal of sustainability, has become a widely accepted paradigm for resource management in Canada and around the world. To ensure sustainability for the forests of northern Ontario, critical minimum levels of natural, social, and economic capital must all be maintained. After these minimum standards have been achieved in each area, an understanding of the tradeoffs which people are willing to make among the various types of capital will aid in selecting resource management strategies which maximise the benefits to society. This project uses a discrete choice experiment to examine the tradeoffs people are willing to make among environmental, social, and economic forest values. The values were specified in a manner similar to the indicators in a criteria and indicator framework. Surveys were mailed to a random sample of 2784 residents of northern Ontario in which respondents were asked to repeatedly select their more preferred forest management Outcome for their respective community. A multinomial logit model was used to estimate part worth utilities for 18 environmental, social, economic, landuse, and access attributes. Based on this model, a computerised decision support system was developed to examine the tradeoffs respondents were willing to make among the various attributes. A collapsed model form was
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".