“I’m Unique!” Children’s Perceptions of Diversity and Representation in the Media
Bibliographic record
Abstract
Recent research shows that television and film media have steadily become more diverse, with more BIPOC (Black, Indigenous, People of Colour) characters present on-screen than ever before (Higginbotham, Zheng, & Uhls, 2020; Johnson, Ruggiero, Wilson, & Buchanan, 2021; Lemish & Johnson, 2019). In this study, eight children ages 5 to 8 from various racial backgrounds participated in interviews and the co-construction of collages to explore and share their perceptions of diversity in some of the media that they consume. Children were presented with ten racially diverse, popular TV and film characters, and were asked to create collages that reflect their likes and dislikes. Participants expressed an appreciation for diverse characters but surprisingly showed little racial preference. The children valued diversity, but as reflected in a character’s personality, design, and combat ability. Diversity mattered to them, but not in some of the ways that I had anticipated. It also revealed that the new sociology of childhood benefits from an intersectional approach and insights from critical race theory.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".