Bibliographic record
Abstract
Archivists need to increase public understanding, support, and engagement in archives to enable archives to fulfil their missions. As one way to increase support, archives have increasingly carved out time and resources for various types of outreach. One important audience that has long been acknowledged is children. In the past, archivists have visited classrooms, brought children to archives, and prepared kits of archival facsimiles or surrogates on websites for children to use with the guidance of teachers. But another way to reach children includes narratives in books, films, and television directed at children. This article explores a number of titles to see whether archives and archivists are accurately portrayed in the narratives. The numbers are few, and the portrayals are generally weak. Two exceptions were books created by an archivist and commissioned by an archives. These two approaches led to significant works that enhance children’s understanding of archives and archivists and lead the way as examples for future archival endeavours to emulate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".