Data and Code for On the Economics of Extinction and Possible Mass Extinctions
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
Human beings’ domination of the planet has not been kind to many species. This is to be expected. Humans have radically altered natural landscapes, harvested heavily from the ocean, and altered the climate in an unprecedented way. Recent concerns over the extent and rate of biodiversity loss have led to renewed interest in extinction outcomes and speculation concerning humans’ potential role in any future mass extinction. In this paper, we discuss the economic causes of extinction in two high-profile cases — Sharks and the North American Buffalo — and then extend our analysis to multiple species and discuss the possibility of mass extinction. Throughout, we present evidence drawn from authoritative data sources with a focus on shark populations to ground our analysis. Despite large gaps in our data, the available evidence reveals a worrisome trend: extinction risks are rising for many species and policymakers have been very slow to react.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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