Bibliographic record
Abstract
This research examined readers' knowledge of popular genres. Participants wrote short essays on fantasy, science fiction, or romance. The similarities among the essays were measured using latent semantic analysis (LSA) and were then analyzed using multidimensional scaling and cluster analysis. The clusters and scales were interpreted by searching for lexical neighbors in the LSA space. The results indicated that there were 4 main types of essays: those that described science and technology as a theme of science fiction, those that described women and courtship as a theme of romance novels, those that discussed narrative and plot structure, and those that discussed feelings depicted in the text or evoked in the reader. Reading experience with the target genre had little detectable effect on the type of essay written for fantasy and science fiction. A second study demonstrated that even self-selected science fiction fans wrote essays comparable to those written by inexperienced readers. However, reading experience did have an effect on the essays written for the romance genre. In particular, essays written by readers with little reading experience with romance tended to describe the theme and plot structure of romance novels, whereas more experienced readers tended to discuss the emotions of the characters and those evoked in the reader. The results provide evidence about the nature of genre knowledge and the mechanisms for its acquisition.
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 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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".