Yes! There are Resilient Generalizations (or “Laws”) in Ecology
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
ABSTRACT It is often argued that ecological communities admit of no useful generalizations or "laws" because these systems are especially prone to contingent historical events. Detractors respond that this argument assumes an overly stringent definition of laws of nature. Under a more relaxed conception, it is argued that ecological laws emerge at the level of communities and elsewhere. A brief review of this debate reveals an issue with deep philosophical roots that is unlikely to be resolved by a better understanding of generalizations in ecology. We therefore propose a strategy for transforming the conceptual question about the nature of ecological laws into a set of empirically tractable hypotheses about the relative re- silience of ecological generalizations across three dimensions: taxonomy, habitat type, and scale. These hypotheses are tested using a survey of 240 meta-analyses in ecology. Our central finding is that generalizations in community ecology are just as prevalent and as resilient as those in population or ecosystem ecology. These findings should help to establish community ecology as a generality-seeking science as opposed to a science of case studies. It also supports the capacity for ecologists, working at any of the three levels, to inform matters of public policy.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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