“Genius,” “Precursors,” and “Great (White) Men” in the History of Archaeology
Bibliographic record
Abstract
Abstract This chapter refers to a number of critical works on the concepts of “genius,” “precursor,” and “great (white) men.” In the past, archaeologists regarded the history of their science as a story of the heroic discoverers who had greatly contributed to the understanding of the past. As a result, disciplinary historiographical accounts adopted the form of discovery stories. The practice of celebrating past glories of science has a number of pernicious effects for the history of science. In particular, historians have typically overemphasized the importance of the so-called precursors and overlooked the contribution of those scholars who have been excluded from the Pantheon of great scientists. To illustrate this problem, this chapter focuses on André Leroi-Gourhan and Annette Laming-Emperaire. Both made significant contributions to the study of cave art. Yet, while Leroi-Gourhan is celebrated as a “pioneer” of structuralism, Laming-Emperaire’s contributions are typically overlooked in archaeological histories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".