Bibliographic record
Abstract
▪ Abstract A new sense of urgency about environmental problems has changed the relationship between ecology, other disciplines, and public policy. Issues of uncertainty and scientific inference now influence public debate and public policy. Considerations that formerly may have appeared to be mere technicalities now may have decisive influence. It is time to re-examine our methods to ensure that they are adequate for these new requirements. When science is used in support of policy-making, it cannot be separated from issues of values and equity. In such a context, the role of specialists diminishes, because nobody can be an expert in all the aspects of complicated environmental, social, ethical, and economic issues. The disciplinary boundaries that have served science so well in the past are not very helpful in coping with the complex problems that face us today, and ecology now finds itself in intense interaction with a host of other disciplines. The next generation of ecologists must be prepared to interact with such disciplines as history, religion, philosophy, geography, economics, and political science. The requisite training must involve not only words, but core skills in these disciplines. A sense of urgency has affected not only ecology but other disciplines that influence environmental problems: they are undergoing a similar transformation of their outlook and objectives.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".