GRAND CHALLENGE No. 4: CURRICULUM DESIGN – Curriculum Matters: Case Studies from Canada and the UK
Bibliographic record
Abstract
Archaeology in the 21st century faces outward more than inward, with many archaeologists working on projects that actively involve young people, descendant communities, diverse colleagues and clients, and the general public. The ways and means of learning and teaching about the past, as outlined in the curricula of primary, secondary, and post-secondary schools, always reflect the prevalent pedagogies of the age. Our paper comments upon two different ways of learning about archaeology. First, it presents an online university graduate program in Canada for post-Baccalaureate Cultural Resource Management (CRM) practitioners and a module on archaeology and education, which may form part of a variety of Master’s degrees in the UK. Second, it examines the ways in which archaeology has been introduced into a range of subjects in the National Curricula of the UK. Our goal is to inspire critical reflection upon the connections between the social milieu in which we teach and learn and the scope and focus of curricula and pedagogy in archaeology. We conclude with comments on current dynamics and desired futures at the fascinating interface of archaeology and education.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".