Bibliographic record
Abstract
The potentials for teaching and learning using technology are tremendous. Now, more than ever before, computers have the ability to spread scholarship around the globe, teach students with new methodologies, and engage with primary resources in ways previously unimaginable. The interest among humanities computing scholars has also grown. In fact at ACH/ALLC last year, Claire Warwick and Ray Siemens et al. gave some excellent papers on the humanities scholar and humanities computing in the 21st century. Additionally, in the most recent version of College and Research Libraries (September 2004), a survey was conducted specifically among historians to determine what electronic resources they use. The interest in this is obviously growing, and the University of Michigan as both a producer of large digital projects as well as a user of such resources is an interesting testing ground for this kind of survey data. Theoretically, Michigan should be a potential model for high usage and innovative research and teaching. In many cases it is; nevertheless, when one looks at the use of electronic resources in the humanities across campus and their use in both the classroom and innovative research, it is not what it could be. The same is true at other universities. At many universities across the U.S. and Canada, including those with similar large scale digitization efforts, use remains relatively low and new potentials of electronic resources remain untapped. Why?
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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.039 | 0.054 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 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".