Faraway, So Close: Seeing the Intimacy in Goodreads Reviews
Bibliographic record
Abstract
Book reviews written by readers and published on digital sites such as Goodreads are a new force in contemporary book culture. This article uses feminist standpoint theory to investigate the language used in Goodreads reviews to better understand how these reviewers articulate intimate reading experiences. A total of 692 reviews of seven bestselling fiction and nonfiction books are analyzed by two methods. The first, thematic content analysis, involves close reading of the reviews. The second, sentiment analysis, is an automated “distant reading” process. These methods prompt us, as researchers, to reflect on the way they foster or inhibit a sense of proximity to readers, even as they reveal predominant features of Goodreads reviews. Together, the methods reveal that 86.1% of Goodreads reviews describe a reading experience, and 68% specifically mention an emotional reaction to the book, with the emotion most intense in reviews of fiction. Reviews also create social connections by mentioning other readers, authors, characters, and people from the reviewer’s life. Through their emotional language and sociality, Goodreads reviews present distinctive, intimate reading practices, constituting a new cultural phenomenon, and a unique opportunity for investigation.
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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 | 0.006 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".