Expanding the scope of affect: taxonomy construction for emotions, tones, and associations
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide an examination of emotional experiences, particularly how they are situated in the readers’ advisory (RA) literature and the literatures from a variety of outside disciplines in order to create taxonomies of affect from this context. Design/methodology/approach The approach of this study is twofold. First, this work reviews the literature on affect in Library and Information Science (LIS) and ancillary disciplines in order to understand the definition of affect. Second, using extant taxonomies and resources noted from the literature review, taxonomies are created for three aspects of affect: emotions, tones, and associations. Findings This paper contextualises and defines affect for the LIS discipline. Further, a result of the work is the creation of three taxonomies through an RA lens by which affective experiences can be classified. The resulting three taxonomies focus on emotion, tone, and associations. Practical implications The taxonomies of emotion, tone, and associations can be applied to the practical work of bibliographic description, helping to expand access and organisation through an affective lens. These taxonomies of affect could be used by readers’ advisors to help readers describe their desired reading experiences. As the taxonomies have been constructed from an RA perspective, and can be applied to the RA literature, they could expand the understanding of RA theory, especially that of appeal. Originality/value This study furthers the exploration of affect in LIS and provides tangible taxonomies of affect for the LIS discipline in an RA context, which have not been previously produced.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".