Bibliographic record
Abstract
While the world economy has become more efficient in one sense, i.e., ecological damage per dollar's worth of economic output, growth in human population size and per-capita production and consumption of goods and services have together far outpaced these gains. Grievous environmental harm has resulted, whether measured in terms of human sustainability through the ecological footprint, or non-human welfare through such indicators as the living planet index and the number of threatened species. Many have therefore called for a reorientation of economic priorities away from growth, and toward equality as a more environmentally-friendly way to enhance human well-being. In this paper, I test the merits of this proposal through analysis of a few key national economic and ecological variables across time and space. The results confirm the hypothesis that equality does far less harm to ecosystems than growth does. In fact, equality seems to benefit one crucial aspect of environmental quality, namely biological diversity.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".