Bibliographic record
Abstract
Participatory Integrated Assessments (PIAs) and community engagement to foster interactive discourses about sustainability have also to confront a need to understand complex and linked social-ecological systems within which sustainability is sought. Over the last 30 years or so, a number of approaches involving collaborative research have been taken under the general rubric of “complexity studies,” and they have been pursued largely independently by groups of natural scientists and mathematicians, or social scientists and historians. There have been at least three overlapping approaches taken. Twelve examples of these are identified and briefly discussed. Applications of complexity studies to PIAs help justify and inform the processes used for assessments, identify key concepts and arguments that the assessments will likely have to address, and provide broad interpretive backgrounds for the larger scale and longer duration systemic processes which nevertheless can impact upon or constrain the phenomena that PIAs consider at smaller scales. A major challenge is how to make these systems perspectives accessible and usable for PIAs. Given the tasks implied by this, a special role is identified for an academic network to keep track of and help develop complex systems thinking while also interpreting it as possible inputs for PIAs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".