Numerical cladistics, an unintentional refuge for phenetics—a reply to Wiley et al.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it