“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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".