The reading background of Goodreads book club members: a female fiction canon?
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 Despite the social, educational and therapeutic benefits of book clubs, little is known about which books participants are likely to have read. In response, the purpose of this paper is to investigate the public bookshelves of those that have joined a group within the Goodreads social network site. Design/methodology/approach Books listed as read by members of 50 large English-language Goodreads groups – with a genre focus or other theme – were compiled by author and title. Findings Recent and youth-oriented fiction dominate the 50 books most read by book club members, whilst almost half are works of literature frequently taught at the secondary and postsecondary level (literary classics). Whilst J.K. Rowling is almost ubiquitous (at least 63 per cent as frequently listed as other authors in any group, including groups for other genres), most authors, including Shakespeare (15 per cent), Goulding (6 per cent) and Hemmingway (9 per cent), are little read by some groups. Nor are individual recent literary prize winners or works in languages other than English frequently read. Research limitations/implications Although these results are derived from a single popular website, knowing more about what book club members are likely to have read should help participants, organisers and moderators. For example, recent literary prize winners might be a good choice, given that few members may have read them. Originality/value This is the first large scale study of book group members’ reading patterns. Whilst typical reading is likely to vary by group theme and average age, there seems to be a mainly female canon of about 14 authors and 19 books that Goodreads book club members are likely to have read.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it