Readers’ histories as a way of studying and understanding multicultural library communities
Bibliographic record
Abstract
Purpose Personal readers’ histories have long had a respected place in reading research. They add a human, personalized dimension to the studies of reading practices, often reported through aggregate findings and generalized conclusions. Moreover, they introduce a private context of readers’ lives, which complements other reading contexts (e.g. historical, socio-economic and cultural) required for an understanding of reading behaviours. The purpose of this paper, based on a selected data set from a larger reading study, is to introduce a gallery of portraits of immigrant readers with the aim to facilitate the library practice with immigrant communities. Design/methodology/approach Qualitative face-to-face intensive interviews with immigrant readers. Findings The knowledge of reading contexts and the opportunity to see readers as individuals rather than anonymous statistics are crucial for librarians who come in contact with multicultural populations. Personal histories can also serve as a step in building interpersonal relationships between librarians and community members. Originality/value The value of the study is in introducing a methodological approach which, through collecting and writing reading histories, allows librarians to gain insight into the cultural practices of multicultural communities and to adjust their work accordingly. This approach can also be used as a prototype for researching other community groups.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".