Bibliographic record
Abstract
Purpose The purpose of this paper is to address a somewhat under‐researched aspect of readers' advisory services in public libraries in North America, namely, readers' advisory for immigrant readers, with a particular emphasis on the readers' advisory interaction/interview. Design/methodology/approach The argument draws on the review of relevant scholarly and professional literature and the author's experience in working with immigrant readers. Findings It is suggested that public libraries in North America are not actively involved in providing readers' advisory services to immigrant readers aside from developing and maintaining multilingual collections. This trend in readers' advisory practices is clearly reflected in professional and scholarly publications of the field. It is argued that personal interactions with immigrant readers, in the context of the readers' advisory interview, can be an efficient way to engage immigrant readers in the life of the host society, thus fostering their socio‐cultural integration beyond information needs and basic coping skills. Originality/value The paper offers practical insights and suggestions for the enhancement of readers' advisory interactions with immigrant readers in public libraries. It also places readers' advisory interactions with immigrant readers in the broader context of readers' advisory practices, public library services to immigrant users, and the theory of readers' advisory interviews.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".