Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate the potential of readers' advisory (RA) in libraries to help immigrants with psychological and socio-cultural adaptation in a new country. Design/methodology/approach – The data were empirically collected from a sample of Russian-speaking immigrant readers residing in the Greater Toronto Area, Ontario, Canada, by means of background surveys and in-depth interviews. Findings – The RA interaction is not merely a conversation about leisure books; it is a powerful intercultural encounter that has the potential to raise the levels of intimacy and attraction between host and immigrant populations, break negative stereotypes, help to build shared networks and create favorable contacts, change intergroup attitudes, and improve readers' mastery of the second language and knowledge of a new country. Originality/value – This article makes a contribution to three areas related to RA. It provides insight into the views and perceptions of RA by a selected group of readers; it gives voice to immigrant readers whose experiences with RA are particularly under-represented in the Library and Information Science literature; and it conceptualizes the RA interaction as an intercultural encounter, using the uncertainty reduction based theory of intercultural adaptation to frame the discussion.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".