Bibliographic record
Abstract
"I use Google for my research, I don't use the library." "Can you teach my students to do legal research only with tools they can access after they leave University?" This paper is about me, the librarian, irritated by the assertion and puzzled by the request. It captures just two of many events on a long and incomplete journey of reimagining librarianship and my changing role as librarian. I would test the assertion, plan the classes, and share those experiences. However, I would be dishonest if I did not also share that I am apprehensive about the results of my tests. What might it mean for me if my clients could be just fine without the library?
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.120 | 0.072 |
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".