Bibliographic record
Abstract
One of the main characteristics of work in the Digital Humanities is collaboration: between individual scholars with complementary expertise, across disciplines, and languages, countries, and continents. Many digital humanists have found that academic cultures can differ widely because cultural factors often weigh on the scope and vision of individuals. For this reason, diversity is at the forefront of the Digital Humanities. This workshop aims to make the participants acquainted with different understandings of diversity in different parts of the globe while considering how more diverse teams contribute to the development of our work. The workshop is directed at anyone with an interest in understanding diversity in digital humanities and creating a welcoming and inclusive DH environment. Conference organizers, leaders in the field, and those who often form part of hiring committees are invited to participate. Everyone is welcome to attend, but we particularly encourage the participation of people who are in privileged positions in academia, GLAM, or similar environments. The workshop will combine presentations, individual work, and roundtables tackling issues such as: · The importance of diversity · Implicit bias · Cultural cloning · Intersectionality · Civil courage · Strategies for becoming more inclusive · Effective collaboration across cultures The workshop will cover gender, ethnic, and linguistic diversity, as well as topics such as ableism, cultural diversity, class, and other matters. The important notion of intercultural communication will also be addressed. During these conversations and exercises, we will have a particular focus on the digital humanities as a working environment, but many of the strategies might be transposed to other areas or to the projects that we develop as digital humanists.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.105 | 0.020 |
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".