Bibliographic record
Abstract
It is not surprising that biologists and philosophers wonder about the nature of species. At first glance we feel assured that we know what we are talking about when it comes to species, but when we take a closer look, matters get more complicated and less obvious. I started my research on species first as a graduate student with Elliott Sober and then as a post-doctorate fellow with David Hull. Both taught me how metaphysics applied to biology can be a satisfying and rewarding form of philosophy. Shortly after my graduate studies, I started thinking more generally about the nature of species. Instead of worrying about their proper biological description or their ontological status, I started to wonder about their role in evolutionary theory. Experts told me that species are units of evolution. I looked at that notion and found its meaning ambiguous and often vague. Perhaps a better understanding of the distinction between species and other types of taxa (genera, families, and so forth) would help. The deeper I dug, the more problems appeared – the distinctions among those types of taxa were far from clear. Soon it became apparent that the entire Linnaean hierarchy of categorical ranks had dubious theoretical underpinnings. What about the procedures we use for naming taxa, since they stem from Linnaeus's system of classification as well? Again, problems began to surface.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.435 | 0.257 |
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".