An Open Lab? The Electronic Textual Cultures Lab in the Evolving Digital Humanities Landscape
Bibliographic record
Abstract
As the scholarly landscape evolves into a more open plain, so do the shapes of institutions, labs, centres, and other places and spaces of research, including those of the digital humanities (DH). The continuing success of such research largely depends on a commitment to open access and open source philosophies that broaden opportunities for a more efficient, productive, and universal design and use of knowledge. The Electronic Textual Cultures Laboratory (ETCL; etcl.uvic.ca) is a collaborative centre for digital and open scholarly practices at the University of Victoria, Canada, that engages with these transformations in knowledge creation through its umbrella organization, the Canadian Social Knowledge Institute (C-SKI), that coordinates and supports open social scholarship activities across three major initiatives: the ETCL itself, the Digital Humanities Summer Institute (DHSI; dhsi.org), and the Implementing New Knowledge Environments (INKE; inke.ca) Partnership, including sub-projects associated with each. Open social scholarship is the practice of creating and disseminating public-facing scholarship through accessible means. Working through C-SKI, we seek ways to engage communities more widely with publicly funded humanities scholarship, such as through research creation and dissemination, mentorship, and skills training.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.062 |
| Scholarly communication | 0.040 | 0.047 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".