Bibliographic record
Abstract
Working through selecting materials for a syllabus, problematic issues arise in both processed and unprocessed materials. There’s a professional urgency in including a trauma-informed framework in instruction and ensuring that people working and viewing collections have the necessary context, preparation, and tools to interpret archival material and manage traumatic responses. Teaching with primary sources requires a knowledge of educational and archival pedagogy. The following paper is a self-reflective exploration into previous work setting a foundation for the models and frameworks still vital in my current role. Enseigner avec des matériels archivistiques en utilisant un cadre qui tient compte des traumatismes RésuméLors de la sélection des matériels pour un plan de cours, des problématiques surgissent à la fois dans les matériels traités et dans les matériels non traités. Il y a une urgence professionnelle d'inclure dans l'enseignement un cadre tenant compte des traumatismes et de veiller à ce que les personnes qui travaillent et consultent les collections disposent du contexte, de la préparation et des outils nécessaires pour interpréter le matériel archivistique et gérer les réactions traumatiques. Enseigner avec des sources primaires nécessite une connaissance de la pédagogie en éducation et en archivistique. Le document suivant est une exploration autoréflexive de travail antérieur qui jette les bases des modèles et des cadres qui sont toujours essentiels dans mon rôle actuel. Mots-clésCadre tenant compte des traumatismes; archives; enseignement
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".