Les modes de conceptualisation des unités d'hérédité au XIXe siècle : Spencer, Haeckel et Elsberg
Bibliographic record
Abstract
Ever since the end of the 19th century, the biological sciences have been preoccupied with the elucidation of the complex mechanisms underlying heredity. They were faced with a fundamental problem: how does a given phenotypic trait (e.g., skin or fur color) correspond to a physical entity, more often than not putative, responsible for its transmission from one generation to the next. The discovery and subsequent characterization of the unit of inheritance (unité d’hérédité) is thus the central focus of research on heredity in many fields, namely genetics, population genetics, molecular biology, and, more recently, genomics. What we now call gene since Johanssen coined the term, however, has a long and troubled past characterized by various successive conceptualizations. These have left sometimes confusing and even contradictory features in modern scientific discourse, of which we intend to understand the origins. The present article aims to examine the different embodiments of the concept unit of inheritance in the works of two key 19th century authors: Spencer and Haeckel. Elsberg, a rival of Haeckel, will also be considered. Using an analysis of indices of conceptualization in discourse, we show the various metaphorical conceptualization modes active in their respective theories and examine how they manifest themselves in English and in French.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".