Economic indicators and their use in sustainable forest management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The economic sustainability literature highlights important theoretical and practical limitations when developing economic indicators to assess sustainable forest management (SFM). Since SFM is multi-disciplinary, no body of theoretical knowledge can embrace all of its dimensions. There is a significant gap between economic theory and management application which will likely remain. For the economic indicators, spatial scales have a very significant impact on the indicator chosen, and there is a danger of not selecting the best indicator simply because there is little or poor-quality data. The use of criteria and indicator frameworks and certification systems is a means to define and assess SFM. However, these frameworks and systems do not address some key conflicts in economic theory. This paper explores these conflicts and their challenges, identifies areas for improvement, and provides some guidance on the use of economic indicators in forest management. The authors conclude that: (1) stakeholder participation is imperative for sfm; (2) all stakeholders need to clearly state their choice of framework before beginning a dialogue on the implementation of economic indicators; (3) new methods for measuring economic sustainability based on the concept of total capital need to be developed; (4) spatial scale must be thoroughly discussed and incorporated into the set of indicators chosen; (5) a selection process needs to be developed to help in balancing the “best” indicators against the “practical” indicators which may not fully address the issues at hand; and (6) the collection and maintenance of appropriate datasets is a priority for the implementation of economic indicators.
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.
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 it