Bibliographic record
Abstract
Theory can be dangerously seductive. Once one has built up an argument that is internally consistent, and once conclusions appear to follow inexorably from premises through clean lines of logic, it is sometimes enticing to conflate logical argument with reality. A good defense against such logical seduction is to let Nature into the conversation. A proper empirical evaluation of the method presented in Chapter 4 would involve an accurately measured environmental gradient involving all of the relevant environmental variables driving natural selection plus measured values of the key functional traits that respond to this selection of all species in the regional pool. This would be replicated in different localities along with evidence of quantitative generality of the community-aggregated traits. Hopefully, this book will have sufficiently convinced you of the potential of the approach that you will contribute to the hard work of assembling such empirical information. When this is done then we will know if the model actually works. I'm easy to seduce. I think that it will work. However, I'm old enough to know the difference between seduction and commitment and I have had enough experience with field ecology to know that it might not work after all. I certainly won't hang myself in the barn if the model fails. As Thomas Henry Huxley famously pointed out, many beautiful theories have been killed by ugly facts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".