IPBES Guidelines for the use of the social-ecological indicator bundles approach
Bibliographic record
Abstract
The Guidelines for the use of social-ecological indicator bundles approach was developed over a series of workshops held in 2015, 2016 and 2017 under the auspices of IPBES to develop an approach that brings together indicators that represent various disciplines, knowledge systems and world views to more fully understand the complex interactions of nature and society. The approach uses narratives guided by key questions to assemble indicators that cover the six elements representing nature and society within the conceptual framework and the interactions between these elements. The guidelines have been provided as a resource for future IPBES assessments since the approach can support the policy-relevance and cohesiveness of key messages for the summary of policy makers within assessments.
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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 | 0.152 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".