Against unifying homology concepts: Redirecting the debate
Bibliographic record
Abstract
The term "homology" is persistently polysemous, defying the expectation that extensive scientific research should yield semantic stability. A common response has been to seek a unification of various prominent definitions. This paper proposes an alternative strategy, based on the insight that scientific concepts function as tools for research: When analyzing various conceptualizations of homology, we should preserve those distinguishing features that support particular research goals. We illustrate the fruitfulness of our strategy by application to two cases. First, we revisit Lankester's celebrated evolutionary reappraisal of homology and argue that his analysis has been distorted by assimilation to modern agendas. His "homogeny" does not mean the same thing as modern evolutionary "homology," and his "homoplasy" is no mere antonym. Instead, Lankester uses both new terms to pose a question that remains strikingly relevant-how do mechanistic and historical causes of morphological resemblance interact? Second, we examine the puzzle of avian digit homology, which exemplifies disciplinary differences in homology conceptualization and assessment. Recent progress has been fueled by the development of new tools within the relevant disciplines (paleontology and developmental biology) and especially by increasing interdisciplinary cooperation. Conceptual unification has played very little role in this work, which instead seeks concrete evolutionary scenarios that integrate all the available evidence. Together these cases indicate the complex relationship between concepts and other tools in homology research.
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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 | 0.076 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.009 | 0.140 |
| Scholarly communication | 0.014 | 0.067 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 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".