Bibliographic record
Abstract
This proposal has twofold foci: a survey of current media discovery websites (MDWs) and a literature review of cross-media appeals. The MDW survey analyzes the appeals and frameworks of 118 MDWs catered to multiple media types (books, movies, music, and video games) while the cross-media appeals were researched through contemporary academic literature (such as Lee et al. 2017, Williamson 2011, and Wyatt 2020). By identifying frameworks of appeal that both exist across media types and are uniquely significant to specific forms of media, (e.g., the appeal of interactivity for video games or danceability for music), connections are created across different media that could not only aid in introducing reluctant readers to books based on what they enjoy in other media but broaden the horizons of all media users. This research lays the groundwork for future discussion for a cross-media advisory tool (CMAT), which would work to aid in full library media advisory and create an accessible tool for laypersons, better enabling them to grow their media literacy skills. Further discussion could include exploring each media type discussed further, proposing future inclusion of other media types (e.g., poetry, audiobooks, graphic novels), and diving deeper into elements that make up individual appeals. For example, when discussing the element of style across media types, how do we consider the differences in language, audio, and visual style, and how do we build connections across them? Future discussions could consider what elements of website design and healthy community building should be included when creating an online media advisory tool.
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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 | 0.027 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.025 | 0.034 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.166 | 0.073 |
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".