Expanding the scope of affect: taxonomy construction for emotions, tones, and associations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it