Bibliographic record
Abstract
There is a new generation of scholarship in the humanities, and it is rooted in twenty-first century technology. In response to what some have called the "crisis in humanities," scholars have begun to tackle their research questions armed with digital tools and a strong sense of collaboration in order to think across disciplines, allow for greater accessibility, and ultimately to create bigger impact. Digital Humanities, or DH, is this exciting and growing field--or maybe methodology--used by humanities scholars to share and create scholarly content.Despite the growing fervour for DH across Canada, many scholars at Queen's have yet to take advantage of the opportunities for research and teaching afforded by DH. I believe that by bringing together Digital Humanities practitioners at Queen's University, more scholars, faculty, and students would learn about and engage in dialogue about fostering and furthering DH scholarship across all disciplines. The best way to begin, I believe, is by hosting THATCamp at Queen's. The Humanities and Technology Camp is an open, inexpensive meeting where humanists and technologists of all skill levels learn and build together in sessions proposed on the spot.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".