Location-based Questions: Types and Implications for Consortial Reference Services
Bibliographic record
Abstract
This paper explores location-based questions asked to a statewide consortial reference service. Literature assumes that non-local librarians would have difficulty responding to questions about places beyond their own library. Based upon the types of questions asked, non-local librarians would benefit from additional local knowledge added to their participating libraries' websites.Cette communication explore les questions géodépendantes posées à un consortium de service de référence offert à l’échelle d’un État. La littérature suppose que les bibliothécaires distants ont plus de difficulté à répondre aux questions non locales. Selon le type de questions posées, les bibliothécaires distants tireraient profit d’un ajout d’information locale sur le site Web des bibliothèques participantes.
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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 | 0.077 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.032 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".