Bibliographic record
Abstract
Improving the health of people and animals as well as improving the health, integrity and sustainability of ecosystems are both laudable and important activities. Can we do both? Clearly, if we wish to have health in the future, then the integrity of ecosystems, which make our lives possible, is relevant. To say we can have sustainable population health without sustainable ecosystems is like saying that we can have a sustainable, healthy heart without a sustainable body, which gives it life and meaning. Yet linking health and ecosystems grammatically – a common and generally well-received notion these days – will do little to link them in real life. Some people would argue that the only ecosystem with integrity is one with no people in it. These people seldom use the word health because they think that health involves value judgements, and integrity is value-free. If anything, integrity is more value-laden, and indeed legally moralistic (which is why it attracts some environmental regulators), than health. Nature may well be value-free, but there is no way to evaluate our status in nature, or to talk about progress, without reference to values. It seems best to some of us to accept this and try to deal with it head-on. There are, quite frankly, no ecosystems that do not, in one way or another, bear the imprint of human meddling.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.375 | 0.174 |
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".