Bibliographic record
Abstract
I suggest two ways to make taxonomic diagnoses more useful: they should state overtly (1) what taxa the new one is diagnosed against, I term this the reference group; and (2) how to identify the reference group from others in the higher-level group to which it belongs, I call this more inclusive taxon the recognition group. Making the reference group identifiable within an as-large-as-possible recognition group increases the usefulness of a taxonomic paper. For diagnoses of 313 newly described insect genera, I assess the taxonomic level and number of genera in both reference and recognition groups. The two were identical in almost half of the cases and were at the same taxonomic level, but the reference group was geographically, ecologically or morphologically more restricted in less than 9%, and the recognition group was at a higher taxonomic level in the remainder. When authors explained how to identify the reference group from a larger recognition group, the number of genera from which the new one could be differentiated increased by a factor of more than four. I make a series of recommendations on how diagnoses can be improved based upon analyses of reference and recognition groups.
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 | 0.060 | 0.277 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.025 | 0.011 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.015 | 0.039 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.037 | 0.031 |
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".