Validation of the Story World Absorption Scale through annotation of online book reviews
Bibliographic record
Abstract
In this paper we present our attempt at validating a self-report instrument developed in the field of empirical literary studies to capture absorption experiences, namely the Story World Absorption Scale (SWAS, Kuijpers, et al. 2014). We used the SWAS as the foundation for a tag set, that targets mentions of absorption in online book reviews. Online book reviews posted on social media platforms are a relatively new form of reader testimonials that can be of use to researchers from different disciplines to investigate reading experience and evaluation, as well as social discourse about reading. This paper discusses the annotation tag set, which was developed through an iterative process, presented alongside a series of inter-annotator agreement studies that show the validity of our annotation process. Finally, it will discuss the validation and reconceptualization of the Story World Absorption construct, where we consider instances of systematic disagreement during annotation and discuss new categories that we added to the tag set that indicate areas where absorption theory may need to be refined.
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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 | 0.081 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".