Review and synthesis of sustainable community indicators used in monitoring forest community sustainability / by Abdul Wahid Khan.
Bibliographic record
Abstract
The objectives of this project are: (1) to review literature related to sustainable community \nindicators that will include studies on: social indicators, sustainable forest management \nindicators, and sustainable community indicators; (2) to develop a viable framework for \nmeasuring sustainability of communities; and (3) to develop a list of sustainable \ncommunity categories and indicators. \nTo better understand the effectiveness of sustainable community indicators in \nmeasuring forest community sustainability, the review of literature is divided into two \nparts. The first part covers frameworks that are used by different scientists in the \ndevelopment of sustainable community indicators. The second part covers three studies \nundertaken in the Canadian Model Forest Program (CMFP) to assess forest community \nsustainability. The first study is based on social indicators, the second study is based on \nsustainable forest management indicators, and the third study is based on sustainable \ncommunity indicators. The main reason for selecting all three studies from the CMFP is \nthat only in the CMFP research is being carried out on community sustainability at the \nlocal level by adopting different approaches (indicated above). The results ofthe studies in \nthe literature review are compared to the results of the study in this project to determine the \neffectiveness of the indicators developed in this study. The indicators developed in this \nstudy focus on sector (population, employment, education, poverty and forest operations) \nsustainability as well as across the sector sustainability. Sector sustainability is achieved by \nassessing the present status of the categories. Across the sector sustainability is assessed by \ntaking into account the impact of each sector on the environment, society and economy \n(ESE). Based on the results of this study, it can be said that every sustainable community \nindicator is a social indicator, but every social indicator is not a sustainable community \nindicator. To achieve sustainable development, it is important to treat the ESE as an \nintegrated unit, and not isolated parts.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".