Bibliographic record
Abstract
This article analyses the origins, development, and impact of Gutenberg-e, a digital publishing program in historical scholarship sponsored by the American Historical Association (AHA), with the support of the Andrew W. Mellon Foundation. Intended as an experiment in developing and legitimizing new modes of historical scholarship, Gutenberg-e quickly evolved, under pressure to become economically sustainable, into a traditional publishing enterprise bent on making books cheaper and paying for itself in the process. Digital technology, which had the power to transform the whole scholarly enterprise, instead became a means to shore up the existing system of scholarly publishing, with all its flaws intact. Though Gutenberg-e has much to teach us about the costs and consequences of that system, especially for the scholars it is meant to serve, it also offers a glimpse of an alternative future. Almost in spite of itself, Gutenberg-e produced a handful of innovative works of digital scholarship, experimented with new forms of scholarly collaboration and community, and highlighted the opportunities of an expanded audience for specialized academic work. These modest achievements suggest the potential of digital technology to create things which scholars value and thereby sustain the scholarly enterprise over the long term.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.042 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".