Reading practices of Spanish-speaking readers in the United States and Canada
Bibliographic record
Abstract
Drawing on a subset of data from a larger survey study of immigrant and migrant Spanish-speaking readers in the United States and Canada, this article explores their pre-immigration reading histories; the role of reading in their lives and personal identities; specific day-to-day characteristics of their reading behaviors, including the frequency and places of reading; and the sources of information that readers use to select their new reads. This study places reading practices in the context of readers’ migration experiences and pressures of adjustment and resettlement. Supported by the review of reading practices in selected countries of origin and by the analysis of the Spanish-speaking communities in the diaspora, this article contributes to the body of knowledge about immigrant and migrant readers. By so doing, it begins to address the gap in knowledge about Spanish-speaking readerships. This gap exists despite the extensive previously published research on Hispanic and Latinx library users, which has focused on their information-seeking behaviors, use of public libraries, language learning programming, and collection development in the Spanish language, without touching on reading practices. It is hoped that this study will contribute to more culturally sensitive reader services in libraries and a better understanding of Spanish-speaking community members by librarians in all types of libraries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".