Digital Humanities Training and Workshops: Anonymized Survey Results
Bibliographic record
Abstract
The Survey on Digital Humanities/Digital Skills Workshops (n = 162) was open from March 11, 2023 to April 8, 2023 as a project under the Implementing New Knowledge Environments (INKE.ca, PI: Ray Siemens) partnership, with support from the Canadian Certificate in Digital Humanities/Certificat canadien en Humanités Numériques (https://ccdhhn.ca/, PI: Laura Estill). This bilingual (English and French) online survey collected qualitative and quantitative responses through SurveyMonkey from respondents who had attended, taught, and/or organized at least one digital humanities (DH) workshop in the period 2019–2023, as well as respondents who decided not to participate in workshops during that period. Aggregated quantitative and anonymized qualitative survey data is available through the St. Francis Xavier University Dataverse, hosted on Borealis, the Canadian Dataverse Repository. This dataset is of potential use to those involved in DH workshops and/or in DH pedagogy more broadly.
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 | 0.033 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".