Numerical cladistics, an unintentional refuge for phenetics—a reply to Wiley et al.
Bibliographic record
Abstract
The assertion that phylogenetic inference algorithms are not authoritarian because results are repeatable, predictable and freely available misses the point that the authority resides in underlying algorithm models that are not cladistic. We show that optimization procedures can group using symplesiomorphy and that optimization is not always equivalent to cladistic argumentation. Because parsimony and Bayesian algorithms can obtain the same answer from the same data set is not evidence that they are Hennigian; examples exist where these methods do not provide the same result from the same data. Using ‘reversals’ as evidence in systematics is problematic because the question, “Reversal to what?” has no straightforward answer. This confusion can be eliminated by recognizing that homologues are the parts of organisms and homologies are the relationships between the parts, and that the latter is a hierarchical concept rather than transformational. We clarify that Hennig’s auxiliary principle pertains to potential synapomorphy, meaning for molecular work that it is the presence of a particular derived nucleotide that is shared in a given position of aligned sequences of two or more taxa that should be considered homologous until proven otherwise, not simply the alignments themselves. We reiterate that not all data are evidence and we specifically reject homoplasy as a source of ‘evidence’ for systematics. We further reject the view that conflict among data should be resolved through methodology. It is the data that should be our primary focus, as it is our attempts to identify and clarify homologues worthy of suggesting relationships (homology) that are primary in systematics.
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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 | 0.036 | 0.113 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.011 | 0.046 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.040 | 0.075 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".