Collaboration in Collections
Bibliographic record
Abstract
This essay presents a rough outline of the “what, how, and why” of the collaborative work done in English 425: “Literature, Archives, and Original Research,” an intensive research undergraduate course at the University of North Carolina at Chapel Hill in the Fall 2024 that focused on juvenilia. The team included a class of thirteen undergraduates (all years; all majors), five PhD students from English and Comparative Literature, one professor from the same department, instructional specialists from Ackland Art Museum, and librarians galore from Wilson Library Special Collections and Davis Library, all at UNC Chapel Hill. We met with two or three museum and four or five library colleagues; but many others, behind the scenes, made our course possible.Eight members of this team tell their story from the points of view of four students, three librarians, and the professor. The projects the class undertook show what can happen when participants believe in each other as partners. They also show how young researchers occupy an exceptional position when it comes to considering what young artists and authors care about and why it matters.
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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.004 | 0.042 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".