Bibliographic record
Abstract
Chapter 1 outlined three main approaches to classification: essentialism, cluster analysis, and the historical approach. Prior to Darwin's work, the prominent view among biologists was that essentialism and cluster analysis are the proper methods for sorting organisms into species. Since then, the historical approach has become the dominant method. Nevertheless, the shift from essentialism and cluster analysis to the historical approach has been controversial. Those biologists who write on the theoretical aspects of biological classification almost universally concur with the shift to the historical approach. Philosophers, however, remain divided. Hull (1976, 1978), Sober (1980, 1984a), Rosenberg (1985a), Williams (1985), and Ereshefsky (1991a) champion the historical approach to biological classification. Kitts and Kitts (1979), Dupré (1981, 1993), Kitcher (1984a, 1984b), and Ruse (1987) favor more qualitative approaches. The first half of this chapter takes up the debate over which approach is appropriate for biological taxonomy. Sections 3.1 and 3.2 outline problems in applying essentialism and cluster analysis to biological taxonomy. Section 3.3 shows why the historical approach is the proper one for biological classification.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
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 teacher head, 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".