Bibliographic record
Abstract
While media studies have frequently assessed the importance of representation, research in this area has often been siloed by institutional and methodological norms that define academics as “gender”, “race”, or “class” scholars, rather than inclusive scholars of all these and more. This paper thus responds to recent calls for more intersectional work by simultaneously addressing the overlapping representations of race, gender, and gamer identity, and their relation to Lorde’s concept of the mythical norm, in the popular webseries, The Guild (YouTube, 2007-2013). Via a detailed, inductive thematic analysis of the show’s two characters of color, Zaboo and Tinkerballa, we find a doubly problematic intersection between standard “gamer identity” tropes and gendered Asian/American stereotypes. The show forecloses on its potential to be truly diverse and reinforces the oppressive, marginalizing practices it tries to mock, suggesting that gaming culture will not change until we address its intersecting axes of power and exclusion. This research also demonstrates how the constructed identity of media audiences-- in this case, stereotypical “gamer” identity-- can exacerbate and reaffirm existing power disparities in representation. We suggest that media scholars remain attentive to the intersecting articulations of media consumer and individual identities in considering how representation can influence systems of inclusion and exclusion, as well as viewers’ lived outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".