University Presses in the Twenty-first Century: The Potential Impact of Big Data and Predictive Analytics on Scholarly Book Marketing
Bibliographic record
Abstract
The use of ‘big data’ and predictive analytics has transformed a sizeable number of industries, from insurance companies to print and e-book online retailers, and both the mass media and the scholarly literature have covered these developments. Online retailers have used big data systems to capture tremendous amounts of data about consumers and their purchases, which has enabled them to use predictive analytics and collaborative filtering systems to make purchase suggestions to consumers. Unfortunately, many university presses—even the largest presses with substantial endowments—have not been able to capitalize on the formidable marketing assets offered by big data and predictive analytics. In this article, the authors review the published literature and significant data sets, and present suggestions for the Canadian and US university press community to launch a non-profit direct-to-consumer Web site generating continuous-time sales and marketing data.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.027 | 0.024 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".