Bibliographic record
Abstract
It is widely accepted that television is a powerful medium and that its influence, particularly on children and young people, can be profound (see for example Canadian Paediatric Society 2003; Strasburger 2004; Matyjas 2015). The representation and categorisation of non-humans in such content may therefore influence a culture’s attitudes towards those species and, by extension, its children’s views. This article investigates animal characters on three hundred and fourteen children’s TV shows across five days of ‘free’ to view UK programming during summer 2020, and is the first study in over twenty-five years (since Elizabeth Paul’s in 1996) to focus specifically on mainstream children’s TV, and the only one to have sole regard for pre- and early primary-age UK viewers. With research clear that the media is so influential, recognising the role of such culture transmission is vital to ‘undo’ unhelpful assumptions about animals that result in their exploitation, and change future norms (Joy 2009). Television media either ignores or misrepresents the subjective reality of many (particularly food) species, but with children preferring anthropomorphised animals to most others (Geerdts, Van de Walle and LoBue 2016), this carries implications in terms of responsibility for our ideas and subsequent treatment of those non-humans in everyday life.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".