Bibliographic record
Abstract
This article is a case study that addresses challenges archivists and introductory composition instructors can experience when working to embed archival and primary source literacy into a course and models how to successfully overcome related obstacles. Building on the excellent work of James Roussain, it employs the archivist-as-educator model not only to teach the students but also to train the disciplinary instructor. Teaching instructors archival and primary source literacy and training them how to teach these types of literacy enhances student success. Acknowledging the literature that discusses the ineffectiveness of one-shot guest lectures, the authors have designed and piloted an archival and primary source literacy toolkit that provides a scalable and effective model for embedding a module and assignment into an introductory composition course at a large research university. The inquiry-based active-learning activities in the toolkit are scaffolded to prepare students for the assignment. Furthermore, the toolkit provides guidance on how instructors and archivists can collaboratively develop the skills they need to successfully embed the module into introductory composition courses.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".