Bibliographic record
Abstract
This paper compares social media traces from Goodreads to data from the MLA International Bibliography and the Open Syllabus Project, in order to better understand the preferences of readers of Victorian literature from different but overlapping communities. We find that the majority of works of Victorian literature that are indicated as being read on Goodreads occur about as often as they are taught or written about in the academy, although books aimed at an adult audience are written about more frequently in peer-reviewed venues. Interestingly, those works that are statistical outliers in terms of their greater popularity with a general audience than an academic audience tend to feature women authors, children’s literature, and works with a strong female protagonist. Turning to an analysis of the written reviews on Goodreads of three outliers that were more popular with a general audience--A Tale of Two Cities, Jane Eyre, and The Secret Garden--we find that readers tend to comment on plot (especially in Dickens), feminist themes (in Jane Eyre), and the importance of characters (in all three works). In conclusion, we suggest ways in which postsecondary teachers might draw on these results to inform their syllabi and formulate strategies for teaching Victorian literature. We argue that in terms of outliers, popular taste in Victorian literature among Goodreads users reflects more general reading preferences among this user group, as readers turn to the Victorian era to read children’s literature and books featuring strong female characters.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".