Bibliographic record
Abstract
Mandating contribution of theses and dissertations (TDs) to university archives and their electronic equivalents (ETDs) to an institutional repository (IR) is common practice. Optimizing workflows for archival print copies while managing electronic copies in an IR can be challenging given such factors as embargoes and the skill sets required to ensure theses and dissertations are accessible, discoverable, and ultimately safely stashed where they belong. As rational processes were gradually developed at the University of Vermont, pitfalls and breakthroughs presented themselves. This article relates our experience launching an ETD mandate, including campus outreach initiatives and improvements to the various related processes (document submission, harvesting, embargo removal). Our journey encompassed a range of experiences that we designated good, bad, or ugly, depending on workflow impact. We realize these are mere labels and that beauty is in the eye of the beholder, especially regarding embargoes.
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 | 0.038 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.032 |
| Scholarly communication | 0.033 | 0.027 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".