Can “Monkey Business” Resolve the Most Contentious Issue in the Convention on Biological Diversity?
Bibliographic record
Abstract
Access to genetic resources and “fair and equitable” sharing of benefits (ABS) is the elusive objective of the 1992 United Nations Convention on Biological Diversity. At the tenth Conference of the Parties, long-standing differences were immortalized through the capacious language of the “Nagoya Protocol [NP] on the Fair and Equitable Sharing of Benefits Arising from the Utilization of Genetic Resources.” Although the NP does not resolve any contentious issue, the opposing narratives of scientist-as-hero and scientist-as-villain are reconciled through “mutually agreed terms” (MAT). The uncontested concept appears twenty-five times and transparency, thrice. An alternative narrative from the economics of information explains how “confidential business information,” subsumed in MAT, frustrates any “fair and equitable” royalty rate. Insights from psychology facilitate the brokerage of a fair and equitable rate and the US Commonwealth of Puerto Rico lends itself to a pilot project on the International Regime on ABS.
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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".