Bibliographic record
Abstract
The University of Michigan Press, with support from the Mellon Foundation, asked John Lavender, of Lavender Consulting, to conduct a review of the American Council of Learned Societies (ACLS) Humanities E-Book collection (HEB) following its launch on Michigan’s new Fulcrum platform. ACLS-HEB is an online collection of over 5,400 high-quality humanities books from over 100 publishers. Now that the market for e-books has matured, part of the review was a comparative study of e-book platforms run by publishers, university presses and e-book vendors; 17 platforms were selected. The review looked at the key features offered by each platform, how they handled searching, content delivery, displaying results, ability to view and download and other key features, there was no attempt to judge the value of the content. Following this review, Michigan Press felt that it would be beneficial to share the results with the wider community. As well as being of interest to publishers, the review will also be relevant for librarians making purchasing decisions and vendors selling e-book services. In addition to synthesizing the results of the e-book platform review, this paper presents a librarian’s perspectives on e-book assessment criteria. Courtney McAllister, Electronic Resources Librarian at Yale University’s Law Library, describes the importance of attributes such as accessibility compliance, library branding, and metadata. Library collections are shaped by a plethora of concerns and criteria. This paper seeks to outline some key elements to consider as part of e-book platform decision-making.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.034 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".