Where Do We Want To Be? Making Sustainability Indicators Integrated, Dynamic and Participatory
Bibliographic record
Abstract
This chapter provides some initial commentary on the development of a sustainability indicator and modeling system in the Georgia Basin, Canada, which has been designed to incorporate a high degree of integration, dynamism and participation. In particular, it is desirable that the development of indicator sets and sustainability indicator systems be made more integrated and dynamic. Governments have typically established sustainability indicator systems in order to support policymaking. Participatory research, including interacting with user groups throughout the research process, helps entrench the relevance and impacts of sustainability indicators. The participatory modelling approach used to design the integrated QUEST models and the creation of a workshop process to envelop QUEST use, facilitate scenario creation and discussions about specific indicators. Context is essential for building the mental models of sustainability that the users develop and adapt in the context of interacting with applications such as QUEST.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".