The Goodreads “Classics”: A Computational Study of Readers, Amazon, and Crowdsourced Amateur Criticism
Bibliographic record
Abstract
This essay examines how Goodreads users define, discuss, and debate “classic” literature by computa-tionally analyzing and close reading more than 120,000 user reviews. We begin by exploring how crowdsourced tagging systems like those found on Goodreads have influenced the evolution of genre among readers and amateur critics, and we highlight the contemporary value of the “classics” in particu-lar. We identify the most commonly tagged “classic” literary works and find that Goodreads users have curated a vision of literature that is less diverse, in terms of the race and ethnicity of authors, than many U.S. high school and college syllabi. Drawing on computational methods such as topic modeling, we point to some of the forces that influence readers’ perceptions, such as schooling and what we call the classic industry — industries that benefit from the reinforcement of works as classics in other mediums and domains like film, television, publishing, and e-commerce (e.g., Goodreads and Amazon). We also high-light themes that users commonly discuss in their reviews (e.g., boring characters) and writing styles that often stand out in them (e.g., conversational and slangy language). Throughout the essay, we make the case that computational methods and internet data, when combined, can help literary critics capture the creative explosion of reader responses and critique algorithmic culture’s effects on literary history.
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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 | 0.009 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".