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
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it