Bibliographic record
Abstract
The title of this column comes from Dr. Carla Hayden’s interview at the RUSA President’s Program at ALA Annual 2018. In the words of the Librarian of Congress, “Reference is, of course, facts. But it is also connecting people to what they need to know, when they need to know it.” Her statement was not just about reference, but about the core purpose of what we do in our libraries—all types of libraries—every day.Listening to the conversation between past ALA President Courtney Young and Dr. Hayden, I was inspired by the themes that cut across library types and connected with the mission of RUSQ. RUSQ is focused on the work that we do, rather than where we perform that work. Place and community are immensely important but our fundamental professional value lies in how we connect with those communities. RUSQ’s columns, research articles, and reviews focus on our shared goals and work so that we can grow professionally through reading a broad range of perspectives, ideas, and research. This quarter’s issue exemplifies this model, presenting content from public and academic librarians on topics ranging from reading to reference to career changes.
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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 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".