Reading tween franchises : cross-media practices and the discourses of tween girlhood
Bibliographic record
Abstract
THESIS ABSTRACT \nThe 'tween' age group, particularly preadolescent females between the ages of 8 \nand 12, constitutes a heavily targeted niche for the branding and cross marketing of \nproducts. Consequently, books aimed at tween readers are often part of cross-media \nfranchises that may include film and television adaptations, affiliated music albums, \nonline fan clubs, video games, clothing, and cosmetics. In this context, representations \nmay be adapted across a number of media forms, and conversely, responses to texts may \nbe facilitated by engagement with diverse media. In light of these trends, this research \nexplores how intersecting discourses of tween girlhood are negotiated through crossmedia \npractices by both producers and consumers of tween franchises. \nThe thesis begins with a review of research from the fields of children's literature \ncriticism, cultural and media studies, girlhood studies, and New Literacies. Building on \nthis review, I outline a theoretical and methodological frame rooted in theories of \ndiscourse as articulated through multimodal design and cross-media play. The analysis \ntraces a cultural history of key discourses in Anglo-American texts for and about \npreadolescent girls. In the following chapters, two tween-oriented cross-media worlds, \nThe Chronicles ofNarnia and Mary-Kate and Ashley Olsen, are used as case studies to \nexamine the roles of multi modal design and cross-media play in the articulation of these \ndiscourses of tween girlhood. Each case study addresses the design of franchise texts (i.e. \nbooks, DVDs, tie-in texts); fan cultures related to these texts; and the responses of eightyear- \nold participants during fieldwork in Toronto, Canada. The conclusion of this thesis \ndiscusses the potential application of this doctoral study in future research on crossmedia \ntexts and practices.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".