Looking toward the Future: A Case Study of Open Source Software in the Humanities.
Bibliographic record
Abstract
2001. Since then, we have been training young humanities scholars in the intricacies of multimedia, markup languages, project management, research methods, and even game theory (Gouglas et al. 2006). Many of our students—like those in other branches of the humanities—have bright futures in education, law, public relations, marketing, and other disciplines. Increasingly, our students find an outlet for their skills in non-profit organizations, many of which have little to no budget for computer technology or support. One of my goals within the Humanities Computing program has been to introduce open source software (OSS) and its politics of community and sharing into my courses. Open source's wide array of software provides computing solutions for numerous contexts. When illustrating the advantages of OSS to my students, the exemplar that I cite is a project on which I myself have been working for a few years now—a manuscript-tracking database for English Studies in Canada (ESC), a journal on whose staff I serve as an associate editor. The ESC Database, as it has come to be called, is an illustrative case study in that it reveals both the successes and challenges of using OSS in academic contexts that are severely limited by staff and by budget. The project itself is still in progress, but far enough along that a scorecard of successes and failures might be instructive to others in similar situations.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".