Put Yourself on the Map! The DH Course Registry Story & its Actors
Bibliographic record
Abstract
Since the turn of the millennium, the digital humanities (DH) have gained more and more momentum, and digital methods, specialised software, new research standards and methodological approaches emerged. Hence, the need for new skill sets and alternative pedagogies arose, which led to an increased offer of digital humanities courses, training events, programmes and degrees with different focus areas. “This expansion [...] has also made it increasingly difficult to maintain an overview, or to feel confident as a potential student of DH that one has found the optimal programme for one’s needs.” Consequently, the DH Course Registry was developed as a central hub to collect information on DH courses to increase the visibility of DH training activities. In collaboration with a visual storyteller and creative technologist, we want to create a poster that will focus on the actors, who tell the story of the DH Course Registry: the users, the database and the API. Additionally, we aim to showcase recent developments via a live demo. 1. The user story The DHCR users can be classified into: (a) Internal users, e.g. the lecturers who feed the registry with course data, the National Moderators who monitor and curate the course entries in their country, and the (user) administrators who maintain the development of the registry; and (b) external users, e.g. students, programme administrators or policymakers, who can make use of the registry for different purposes. 2. The database & API story The registry offers access to a Digital Humanities course database: Users can browse the platform and use filters (e.g. country, city, language, ECTS credits, degrees, TaDiRAH, etc.) to narrow down their search results. The API enables access to the (meta)data collected (see fig. 1): interested entities can undertake diachronic research and develop various web applications to tell the DH Course Registry data’s story, see the ACDH-CH Hackathon as an example. Depending on the point of view and the researchers’ interest, various other research scenarios and questions can be elaborated. 3. Challenges in the narrative The initiative is a nutrient medium inspiring its actors to embrace shared values when reaching out to other digital humanists, but it bears some limitations. If a country is not monitored by a national moderator and there are no contributors feeding data into the registry, there is no DH data story to tell. Hence, the four pillars of dissemination (website, notification, social media, events) play an important role in keeping existing users engaged and attracting new ones. Consequently, the platform could never tell the story of the establishment of DH-training activities as a stand-alone resource, it is amplified in a reciprocal process, enhanced by its users curating the platform. The more users are attracted, the more data can be collected, and the more stories can be told.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.282 | 0.146 |
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".