Creating a Consolidated Online Catalogue for the University Press Community
Bibliographic record
Abstract
This essay derives from a feasibility study into the possibility of creating a consolidated online catalogue for university presses and was underwritten by the Andrew W. Mellon Foundation and sponsored by The Monterey Institute for Technology and Education; the essay is an abridgement of the report submitted to Mellon and MITE. For more information on the background, see the acknowledgments and disclosures at the end of this paper. The aim of this study was to identify a means by which university presses could sell more books and, specifically, whether the creation of a consolidated online catalogue of press titles would help in this regard. The presses at this time vary considerably in terms of size, resources, and familiarity with online marketing, though all of them are active to some degree online and most identify Amazon as their largest customer. Despite this activity, however, and even for the very largest presses, a press-wide catalogue would augment sales by exploiting greater scale, enabling mor...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.049 | 0.083 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".