‘A pretty sublime mix of WTF and OMG’. Four explorations into the practice of evaluation on online book reviewing platforms
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
The article uses a corpus workbench (Sketch Engine) to investigate practices of evaluation in online book reviews. The reviews were taken from Goodreads, Amazon, bol.com and a number of Dutch online book discussion platforms. We look at tools that have been used to study online book reviews. Then we investigate our own collection of reviews. Findings suggest (1) that online reviews are not just centred on the reviewers’ experiences but include solid discussion of the merits of books; (2) that reviewers of suspense prefer plot and character while reviewers of literary books prefer style and story; (3) that literal and metaphorical phrases referring to the body are often used in describing positive reading experiences; and (4) that positive reviews recount parts of the story, while negative reviews try to explain why the book was a disappointment.
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| 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