Diamonds in the Dust: The Underlying Pedagogical Value of Old Material Collections
Bibliographic record
Abstract
The ethics of studying or collecting unprovenanced material is intrinsically tied to the issue of looting. In recent years looting has become increasingly facilitated by the current political instability in various parts of the world, with looted objects often serving as a highly lucrative source of income for insurgent or even terrorist groups. As international statutes and industry protocols aim to alleviate further threats to cultural heritage, consideration is also due to the future of the vast collections of unprovenanced and subsidiary material acquired through decades of cultural tourism and incipient archaeological exploration. A survey of Canadian and American academic institutions was conducted to determine the extent of department-held material collections, as well as the current pedagogical and research purposes to which they are directed.
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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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.044 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".