Reflections on the concept of ‘precursor’: Juan de Vilanova and the discovery of Altamira
Bibliographic record
Abstract
Considering the case of Juan de Vilanova y Piera, often celebrated as the first scientist to accept the prehistoric antiquity of palaeolithic paintings, we explore some of the problems related to the concept of ‘precursor’ in the field of the history of science. In the first section, we propose a brief history of this notion focusing on those authors who have reflected critically on the meaning of predecessors. In the second section, the example of Vilanova illustrates the ways in which historians of science have created precursors. From the vantage of modern science, precursors have traditionally been defined as those who first indicated or announced ideas or theories later accepted by the scientific community. As a result, they have been represented as ‘heroes’ struggling hard to defeat the ignorance of their time. As the case of Juan de Vilanova illustrates, this traditional view is unsatisfactory in many ways. For this reason we consider in the third section a number of methodological strategies to promote a more adequate approach to pioneers. In particular, we suggest that the best way to surmount hagiographical approaches to past scientists is to put them in their own intellectual and historical contexts.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".