Bibliographic record
Abstract
BookTok, a TikTok community where creators discuss and review books, influences the publishing industry as books that gain popularity on TikTok have seen mainstream success. BookTok is believed to be a diverse space where stories about marginalized identities are celebrated. This is in opposition to the traditional publishing world that is dominated by White, heterosexual, cis-gendered men. However, despite misconceptions, online spaces are notably homogeneous, and TikTok does not appear to diverge from these patterns. By analyzing 55 TikTok videos collected from the BookTok community, this study analyzes the race, gender, and sexual orientation of TikTok creators, authors, and main characters of BookTok books. This article aims to understand the effects social media applications such as TikTok have on the publishing world and to understand BookTok’s relation to diversity. While there is more gender equity among the authors of BookTok than in the traditional publishing world, there continues to be a deficiency in the prevalence of marginalized authors on the platform. Although women creators and women authors are popular on the app, most of these women are White. In addition, the authors who are most discussed on BookTok do not typically include persons of color or members of the LGBTQ+ community. The tendency for authors to write about their own experiences results in there being few characters of color and few books about members of the LGBTQ+ community. Publishing houses should prioritize increased collaboration with authors of color and LGBTQ+ authors, while also using BookTok to promote and advertise their work.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".