Intriguing New Model for Improved Visibility and Access to Theses and Dissertations
Bibliographic record
Abstract
The George A. Smathers Libraries at the University of Florida (UF) are participating in an innovative program to explore whether making electronic theses and dissertations (ETDs) available in print through online retail sites can have positive impacts for graduates, the university, and the general public.Digitization and metadata enhancement have improved discoverability and ease of access for ETDs in the Institutional Repository at the University of Florida (IR@UF).However, through this new program, research can be shared widely beyond academe with practitioners, corporate researchers, independent scholars, and international readers.This paper will describe how the Smathers Libraries have worked with a corporate partner, BiblioLabs, to leverage online retailers' discovery engines to promote print versions of ETDs while alerting readers to the free digital versions available in the IR@UF.This paper will also share how alumni, current graduate students, and other campus stakeholders have responded to the pilot of this new service.The libraries are monitoring referred traffic to the IR and sales data.UF is the first university to contribute content to this effort, but we expect others to follow suit if the data supports the expectations of the university, the Smathers Libraries, and our graduates.
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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.028 | 0.052 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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".