Whither Library Data: The Withering of Research Information about Public Libraries in the <scp>US</scp>
Bibliographic record
Abstract
ABSTRACT The field of information science has deep roots in library science, and the two are often discussed as one discipline (i.e., library and information science). In addition, library‐centric research has often been included in the Annual Meetings of ASIS&T. Nonetheless, research centered in and around libraries frequently faces many challenges in our discipline—including a sense of disrespect or under‐appreciation for work in and around libraries and library workers. This panel focuses on the challenges of finding, collecting, and analyzing library‐centered data. We discuss these hurdles to push for a more productive research landscape around library science. Five panelists will descript their work and the obstacles they have encountered. The panel will include an open discussion with the audience, focusing on how we as a research community might alleviate these challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.028 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".