Technology and Crafts in Archaeology Critical review over “Archaeological Approaches to Technology”
Bibliographic record
Abstract
“Archaeological Approaches to Technology” by Heather Margaret-Louise Miller, professor at the University of Toronto is truly an updated survey on technology and crafts in archaeology that witnesses the hard efforts of the author. A critical review of the Persian translation of the book will lead us to improve future translations in this area of study. To do this critical review, the author of this paper first read the whole book in a precise way and then made some notes over those parts of the book that were necessary in terms of contents or in editorial aspects. Then to correct some downsides, it was necessary to compare the Persian translation with the original texts, and finally some suggestions have been made for improving the translation. One of the most notable aspects of the book is its wide references over different areas of studies in each section. This could be very useful for graduate students who seek to work on crafts and technology in archaeology. Persian translation of the book has been done in a good way by Vahid Asgarpoor, assistant professor of Art at University of Tabriz, a young and active archaeologist. From the technical point of view, the book is printed and edited in a professional way, the combination of which with its simple translation would probably satisfy the readers. The biggest shortage of the translation is that the translator sticks too much to the main texts and its structure in a way that in some parts of the book the grammar is more like the original language than to Persian.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".