Filling in the Gaps: DDA e-books pilot project in Religious Studies and History.
Bibliographic record
Abstract
Historically, faculty members in the department of Religious Studies have had a very strong influence on the library’s collection development policy. However, a survey of graduate students and faculty performed in 2011-2012 for the Council of Ontario University’s graduate program accreditation revealed surprising information regarding their satisfaction with the religious studies collection. In response to feedback from the survey and in a quest to improve the relevance of the religious studies collection to our community, the University of Ottawa worked with our book vendor Yankee Book Peddler (YBP) to roll out a demand-driven e-books acquisition project in two subject areas: religious studies (including theology) and history. The specific goals of the project were: (1) To fill in historical titles that we had not been able to purchase due to several decades of low funding of the University of Ottawa Library; (2) To test whether e-books really were as undesirable as faculty members claimed they were; (3) To understand where the collection gaps were based on community demand; (4) To test the current collection development policies in religious studies and history; and (5) To provide an evidence-based argument for collecting or not collecting e-books as a format. This presentation will detail the conceptual and technical development of the plan and will offer observations from nine months of implementation with an emphasis on the religious studies and theological collections areas. Did the project achieve its goals? How useful were the data gathered from this project? How will these data inform our future strategies regarding collection development for Religious Studies? These questions and others will be explored in the project’s summary.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".