Bibliographic record
Abstract
Purpose The concept of appeal has traditionally been considered a cornerstone of readers' advisory (RA). Critically revising the foundational works on appeal that have guided RA for more than two decades, this article aims to discuss the best ways to approach teaching RA as contextually grounded practice. Design/methodology/approach The paper uses a critical review of RA foundational works and of selected RA tools and publications; a comparative analysis of two empirically generated models of reading; and discourse on the possible application of research interviewing methods to the RA interview. Findings Given the disclosed unutilized potential of the existing theory of appeal and in light of recent empirical research, the concept of appeal should become less compartmentalized and should be broadened to include the reader and his or her reading context. Reading studies should be seen as directly relevant to understanding appeal. The SQUIN (single question aimed at inducing narrative) technique, borrowed from narrative research interviews, can be used in RA interviews to collect contextually grounded information about the appeal of reading. Originality/value This article will be of interest to LIS educators, practising readers' advisors, other public services librarians, reading scholars, and library and information science students. It takes a radically different approach to the concept of appeal, which has remained relatively stable since its conception in 1989, and uses it to propose not only a more holistic approach to RA but also some practical ways to teach it to future readers' advisors.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".