Illusions of a “Bond”: tagging cultural products across online platforms
Bibliographic record
Abstract
Purpose Most studies pertaining to social tagging focus on one platform or platform type, thus limiting the scope of their findings. The purpose of this paper is to explore social tagging practices across four platforms in relation to cultural products associated with the book Casino Royale , by Ian Fleming. Design/methodology/approach A layered and nested case study approach was used to analyse data from four online platforms: Goodreads, Last.fm, WordPress, and public library social discovery platforms. The top-level case study focuses on the book Casino Royale , by Ian Fleming and its derivative products. The analysis of tagging practices in each of the four online platforms is nested within the top-level case study. Casino Royale was conceptualized as a cultural product (the book), its derived products (e.g. movies, theme songs), as well as a keyword in blogs. A qualitative, inductive, and context-specific approach was chosen to identify commonalities in tagging practices across platforms whilst taking into account the uniqueness of each platform. Findings The four platforms comprise different communities of users, each platform with its own cultural norms and tagging practices. Traditional access points in the library catalogues focused on the subject, location, and fictitious characters of the book. User-generated content across the four platforms emphasized historical events and periods related to the book, and highlighted more subjective access points, such as recommendations, tone, mood, reaction, and reading experience. Revealing shifts occur in the tags between the original book and its cultural derivatives: Goodreads and library catalogues focus almost exclusively on the book, while Last.fm and WordPress make in addition cross-references to a wider range of different cultural products, including books, movies, and music. The analyses also yield apparent similarities in certain platforms, such as recurring terms, phrasing and composite or multifaceted tags, as well as a strong presence of genre-related terms for the book and music. Originality/value The layered and nested case study approach presents a more comprehensive theoretical viewpoint and methodological framework by which to explore the study of user-generated metadata pertaining to a range of related cultural products across a variety of online platforms.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".