Affective taxomonies of the reading experience: Using user‐generated reviews for readers' advisory
Bibliographic record
Abstract
ABSTRACT This paper examines affect in the reading experience to help both readers' advisors and readers as they work to suggest books to readers and choose books for their individual context. Using Grounded Theory analysis of 536 user‐generated reviews from 831 bibliographic records of a selection of fiction titles (n=22) in Canadian public libraries whose catalogues allow for the inclusion of user content were analyzed for affective content. The content of the reviews was coded into three categories, Emotions, Tones, and Associations and taxonomies were developed. Emotions are represented by 9 basic categories, and 44 unique emotions, Tones by 11 basic categories and 141 unique tones, and Associations by 7 basic categories and 31 unique associations. Affective access points can serve as an important addition to the bibliographic records for works of fiction and it is suggested that the derived taxonomies could be used as facets by which to narrow the results of a search for readers' advisory efforts in public 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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".