The education of public librarians to serve leisure readers in the United States, Canada and Europe
Bibliographic record
Abstract
Purpose To provide a comparative review of the teaching of Readers' Advisory Services in schools of library and information science in selected schools in the USA, Canada and Europe. Design/methodology/approach After reviewing the literature, schools are selected based on their known activity in providing readers' advisory service courses or on their national ranking (in the case of US schools) to provide a snapshot of current level of readers' advisory instruction. Findings Instruction in readers' advisory services is a very small part of the total curriculum in schools examined. Librarians who wish to gain more insight to readers' advisory services must depend on continuing education opportunities, such as workshops and conference programs, not on courses in the curriculum of schools of library and information science. Originality/value This paper raises questions as to the relationship between library and information science curricula and the needs of practicing librarians to provide services to leisure readers. It finds that, despite an increased interest in providing readers' advisory services in libraries, library education is not responding to that need and continuing education and training programs are essential to providing librarians who are well prepared to serve leisure readers. For schools which are contemplating adding coursework in these areas, the case studies detail courses as they are offered at other institutions.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".