Inside Digital Dinah Craik: Feminist Pedagogy, Cognitive Apprenticeship, and the TEI
Bibliographic record
Abstract
In this essay, we describe our collaborative work as students and teachers on a TEI edition of Dinah Mulock Craik’s correspondence. Inside Digital Dinah Craik, our pedagogy is collaborative and inclusive, attentive to the material conditions of both the text and our labor, and is reproducible. We follow a cognitive apprenticeship model of education that emphasizes a community of practice where learners become increasingly proficient until, ideally, they are no longer apprentices but genuine collaborators. In this paper, we demonstrate how the five stages of apprenticeship learning—modeling, approximating, fading, self-directed learning, and generalizing (Hansman 2001, 47)—help us to foster what scholars such as Anne Balsamo, Elizabeth Losh, Jacqueline Wernimont, Laura Wexler, and Hong-An Wu call the “foundational ethical principles” (Balsamo 2011, 162–3) and “feminist virtues” (Losh et al. para. 26) of collaboration—confidence, humility, flexibility, integrity, and intellectual generosity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".