The Equilibrium Conundrum
Bibliographic record
Abstract
The idea that natural systems tend to be at equilibrium dates back to the origin of the field of ecology and continues to underlie most ecological theory. However, empirical evidence for equilibrium dynamics in nature and in experiments is surprisingly elusive. Here, we address this conundrum by first exploring the history of equilibrium in ecological theory and the evidence for equilibrium dynamics in natural systems. We then search the literature to quantify how empiricists deal with equilibrium in their research and address barriers to integrating the concept of equilibrium into empirical work by providing step-by-step instructions for determining whether a population is at equilibrium. Next, we lay out three ways that equilibrium is embedded in theory, and for each, outline when meeting the equilibrium assumption in empirical tests is critical for scientific inference, and when it may be possible to relax this assumption. And finally, we present concrete steps that empiricists and theoreticians can each take in order to meet in the middle when it comes to equilibrium. We hope that this paper will stimulate new discussions from researchers from across the theory-empirical divide about this longstanding issue.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".