Bibliographic record
Abstract
Over the past quarter century, a new scientific activity has emerged: collective assessments by large numbers of scientists from different disciplines combining their expertise to better understand human interrelations with nature and to inform policy. The Millennium Ecosystem Assessment exceeded all such assessments before it in both the breadth of its coverage and the depth of its analysis of socioecological system dynamics. The findings are not encouraging. Nearly all ecosystems are being degraded and will continue to be degraded for decades to come even if policy changes are initiated now. For scientists participating in the assessment, the MA had another disconcerting aspect. It clearly shows that our fragmented, disciplinary knowledges cannot simply be combined to form an understanding of a whole complex system. Counterbalancing the despair of the findings and scientific difficulties of aggregating specialized knowledges, the MA demonstrated the potential of a deliberative democratic approach to grappling with complex problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.066 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.016 | 0.035 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.016 |
| 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".