The rise and fall of Jackdaws: lessons for designing source collections to teach history
Bibliographic record
Abstract
For more than a century, educators and historians have advocated for the importance of using primary sources for teaching history. One of the most innovative and popular collections of sources in the past 60 years were Jackdaws: Collections of Contemporary Documents, which were published by Jonathan Cape between 1963 and 1977. Jackdaws are folders that contain reproductions of primary and secondary sources focused on significant historical events, people, developments, themes and topics in history. In this article, I provide a brief history of Jackdaws, and explain why they were initially popular as a learning resource for teaching history, and why their popularity waned in the mid-to-late 1970s. I conclude by highlighting several lessons that can be learned from the rise and fall of Jackdaws that might help history teachers and educators design collections of primary and secondary sources for teaching history. The three reasons that best explain why Jackdaws became popular learning resources in school history classrooms between 1963 and 1977 are that they were aligned with innovative educational theories at the time, they were flexible and adaptable to diverse contexts, and they were interesting and exciting for students. Despite being heralded as a groundbreaking and revolutionary resource for teaching history, Jackdaws failed to transform history teaching and learning for four main reasons: they were too difficult for some students; they were an awkward fit for some school history curricula; they were expensive and difficult to manage; and there was a lack of pedagogical supports to help teachers use them effectively.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.014 | 0.033 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".