From Paper to Pixels: Challenges and Solutions on the Way to ETDs
Bibliographic record
Abstract
This poster was presented at the Canadian ETD and Open Repositories Workshop, Carleton University, May 10-11 2010 and at ETD 2010, Austin TX, June 16-18 2010. The poster outlines the process of developing an ETD program from pilot project to mandatory ETD submission, including issues encountered and solutions developed by the UBC Library and the Faculty of Graduate Studies. It touches on software, preservation, copyright and embargoes, and also highlights our current project to digitize all of UBCs theses retrospectively and make them available in cIRcle, our institutional repository.
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 | 0.048 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.038 | 0.049 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.118 | 0.040 |
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".