Teaching Egyptian History: Some Discipline-Specific Pedagogical Notes
Bibliographic record
Abstract
Abstract This paper was originally given at the professional workshop In Search of Egypt's Past: Problems and Perspectives of the Historiography of Ancient Egypt; A North American workshop at the University of British Columbia, Vancouver, inaugurating the Journal of Egyptian History, April 23–24, 2008; most of the remaining papers of which will appear in Fascicle 2 of this journal. While many Egyptologists teach Egyptian history, we often fail to carefully conceive of just what this means. Teaching history is more than conveying facts about a time period, it is also teaching how to analyze and (re)construct history. Our classes may often teach this aspect as well, but is it explicit? And are we equipping graduate students with the ability to both do and teach history well? This training has a direct impact on their employability as well as their scholarship. A survey and study of History Department outcomes reveals areas we can improve our history teaching and our training of graduate students. Moreover, as Egyptologists, we have a significant offering to make to teaching history.
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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 | 0.003 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".