Transformation Series as an Ideographic Character Concept
Bibliographic record
Abstract
An ideographic concept of character is indispensable to phylogenetic inference. Hennig proposed that characters be conceptualized as "transformation series", a proposal that is firmly grounded in evolutionary theory and consistent with the method of inferring transformation events as evidence of phylogenetic propinquity. Nevertheless, that concept is usually overlooked or rejected in favor of others based on similarity. Here we explicate Hennig's definition of character as an ideographic concept in the science of phylogenetic systematics. As transformation series, characters are historical individuals akin to species and clades. As such, the related concept of homology refers to a historical identity relation and is not equivalent to or synonymous with synapomorphy. The distinction between primary and secondary homology is dismissed on the grounds that it conflates the concept of homology with the discovery operations used to detect instances of that concept. Although concern for character dependence is generally valid, it is often misplaced, focusing on functional or developmental correlation (both of which are irrelevant in phylogenetic systematics but may be valid in other fields) instead of the historical/transformational independence relevant to phylogenetic inference. As an ideographic science concerned with concrete objects and events (i.e. individuals), intensionally and extensionally defined properties are inconsistent with the individuation of characters for phylogenetic analysis, the utility of properties being limited to communicating results and facilitating future rounds of testing.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".