Bibliographic record
Abstract
The term levels of analysis has been used in several ways: to distinguish between ultimate and proximate levels, to categorize different kinds of research questions and to differentiate levels of reductionism. Because questions regarding ultimate function and proximate mechanisms are logically distinct, I suggest that distinguishing between these two levels is the best use of the term. Integrating across levels in research has potential risks, but many benefits. Consideration at one level can help generate novel hypotheses at the other, define categories of behaviour and set criteria that must be addressed. Taking an adaptationist stance thus strengthens research on proximate mechanisms. Similarly, it is critical for researchers studying adaptation and function to have detailed knowledge of proximate mechanisms that may constrain or modulate evolutionary processes. Despite the benefits of integrating across ultimate and proximate levels, failure to clearly identify levels of analysis, and whether or not hypotheses are exclusive alternatives, can create false debates. Such non-alternative hypotheses may occur between or within levels, and are not limited to integrative approaches. In this review, I survey different uses of the term levels of analysis and the benefits of integration, and highlight examples of false debate within and between levels. The best integrative biology reciprocally uses ultimate and proximate hypotheses to generate a more complete understanding of behaviour.
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 | 0.040 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.056 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".