Book review: The prehistoric apprentice: Investigating apprenticeship, know-how and expertise in prehistoric technologies; L’apprenti préhistorique: Appréhender l’apprentissage, les savoir-faire et l’expertise à travers les productions techniques des soci
Bibliographic record
Abstract
Describing cultural change and variability and inferring sociocultural dynamics about past people and communities may be among archaeology’s main goals as a field of practice. In this regard, the concept of skill has proved its usefulness to, time and again, expand the breath of archaeologists and lithic technologists’ analyses. It covers a wide range of applications, from apprenticeship, cognition, paleo-sociology, spatial organization. It is one of the main causes for material culture variability, up there with raw material constraints, design, technological organization or cultural norms. Yet, while skill has certainly been the focus of some research in the last decades, it remains quite peripheral, when considering how central the concept should be to technological inquiries. Whatever the reasons may be, this book, edited by Laurent Klaric and fully bilingual (French and English), aims at changing that, and argues for skill to become a central concern in lithic technology. Its chapters do so strongly and the end-result is a book that should become a reference for lithic technologists, whatever their research interests or schools of thought may be.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".