On Theory in Ecology: Another Perspective
Bibliographic record
Abstract
We agree with Marquet and colleagues (2014) that the balance between theory and data is an important one. However, their description of what constitutes good theory in ecology ignores the most important characteristic of successful theory—that it accurately and precisely describes the way the world works. Scheiner (2013) argued that progress in ecology has been hindered by the fact that ecological research is rarely grounded in theory. Marquet and colleagues’ call for increased emphasis on theory is an important one, but we disagree with their description of good theory. Marquet and colleagues identify efficiency as a key criterion for assessing theory and define efficient theories as (a) being built from first principles, (b) incorporating mathematics, (c) having few “free parameters,” (d) having few assumptions, (e) making many predictions, and (f) being parsimonious. We suggest that any assessment of theory that does not use the ability of the theory to predict independent observations as its foundation is fundamentally flawed. The most important criterion by which to judge a theory is how well its predictions match observations.
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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.060 |
| Scholarly communication | 0.011 | 0.029 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.010 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 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".