The MoEML Pedagogical Partnership Program
Bibliographic record
Abstract
Since 2014, The Map of Early Modern London (MoEML) has partnered with professors and students around the world in a unique collaboration between a digital humanities (DH) project and humanities classrooms. The model we have developed addresses a sustainability challenge for DH projects, provides professors with a way of meeting administrative demands for engaged learning, and gives students a high-stakes research-based learning opportunity with the potential for an open-access, peer–reviewed publication. The MoEML Pedagogical Partnership Project emerged from a confluence of problems and opportunities. One longstanding problem for DH practitioners is project-based: how do we sustain the projects already begun? Another problem emerges as DH moves out of the big tent and sets up camp in humanities classrooms at smaller, non-R1 institutions. Also, for scholars not trained in the technologies that drive many DH projects, crossing the analog-digital divide might be daunting and discourage them from contributing to DH projects. To address these challenges, the MoEML Pedagogical Partnership takes Research-Based Learning (RBL) models and turns them into high-profile publication opportunities, mobilizing ubiquitous social networking and communication technologies to connect the project with the new demographic of student contributors. This essay will highlight how digital projects and digi-curious professors can collaborate to develop innovative pedagogical practices that provide projects with content, enliven professors’ pedagogy, and invite students to acquire scholarly research skills, gain digital literacy, and engage in an interdisciplinary and international collaboration. We argue that DH projects can be used innovatively and effectively in the classroom to promote RBL. At the same time, DH projects–open-access ones in particular–can provide a home both for humanities research and for the fruits of digital pedagogy across a wide range of institutional settings.
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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 | 0.006 | 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 | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.166 | 0.057 |
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".