Archaeology education. Our governmental concern? A study on the state of affairs on policiy stakeholders' perspectives regarding the inclusion of Archaeology into Dutch primary education.
Bibliographic record
Abstract
The relation between archaeology and education contains a paradox. Where archaeologists have advocated the importance of education for archaeology, this advocation has not been adapted by policy stakeholders, in order to develop archaeology education programs. Three studies in Canada, the United States and United Kingdom have entailed that the relation\nbetween archaeology and education has poorly been investigated. This research builds upon the results on the other three studies to start the investigation on the state of affairs on the inclusion of archaeology into primary education in the Netherlands by investigating policy stakeholders perspectives. These values are investigated by interviews among representa-\ntives of the Ministry of Education, Culture and Science, provincial heritage institutes and museums, and placed into broader perspective by analyzing the results of monitor surveys\non three history and culture education programs, and two legislative restrictions. Then, the combination of interviews and document analysis results in a synthesis where an alternative approach for archaeology education is presented for archaeologists, policy stakeholders and Primary school teachers in the Netherlands . The study ends with the request for further research that is built upon the results presented here.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".