A Post Fifty Shades of Grey Examination of Canadian Public Library Collections
Bibliographic record
Abstract
The phenomenon surrounding Fifty Shades of Grey (FSOG) created a new and public conversation about erotica and erotic romance literature. Although the phenomenon surrounding FSOG has faded, the sub-genre of erotica/erotic romance is still of interest. Traditional and nontraditional LIS review sources were examined for erotica/erotic romance titles to determine characteristics of titles reviewed, trends seen in the reviews, and to uncover the uptake of reviewed materials in Canadian libraries. Findings show some distinct characteristics in titles along with an influence of FSOG in the reviews, and a mismatch between the review sources examined and an example of a public library’s collection.Le phénomène entourant Fifty Shades of Grey (FSOG) a créé une nouvelle discussion publique à propos de l’érotisme et de la littérature érotique. Bien que le phénomène entourant le FSOG ait disparu, le sous-genre de l'érotisme et de la littérature érotique est toujours d'intérêt. Nous avons examiné les sources d'examen des sciences de l’information traditionnelles et non traditionnelles dans le but de déterminer les caractéristiques des titres examinés, les tendances observées dans les revues et de découvrir l'utilisation des documents examinés dans les bibliothèques canadiennes. Les résultats montrent des caractéristiques distinctes dans les titres ainsi qu'une influence du FSOG dans les revues, et une inadéquation entre les sources examinées et un exemple de collection dans une bibliothèque publique.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.035 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".