Value exclusion in social–scientific approaches for assessing and valuing ecosystem features: Implications for behavioral compliance
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
Abstract Value inclusion is critical for effective ecosystem science policy and largely emerged from critiques of the value-exclusionary attributes of ecological and economic approaches to value assessments and valuations. But whether and how value is excluded during social–scientific approaches to the assessments and valuations of ecosystem features has not received adequate attention. We identify and discuss instances of when and how value is excluded during social–scientific approaches to the assessments and valuations of ecosystem features to which people ascribe value. We illustrate the implications of value exclusion on social compliance with ecosystem management and policy recommendations, a vital overlooked aspect of policy effectiveness. We also extend the meaning of value exclusion beyond value omission to include misidentification and misattribution of salience to valued ecosystem features. We offer suggestions for enabling value inclusion where ways to minimize exclusion are inapparent.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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