Critical Media Analysis in Teacher Education: Exploring Language-Learners’ Identity Through Mediated Images of a Non-Native Speaker of English
Bibliographic record
Abstract
Media literacy education has become increasingly present in curricular initiatives around the world as media saturate our cultural environments. For second-language teachers and teacher educators whose practice centers on language, communication, and culture, the need to address media as a pedagogical site of critique is imperative. In this article, I introduce critical media analysis (CMA) as a tool that cultivates discussion of language-learners’ identities as they are shaped by popular media. I present CMA in the context of critical language studies and communication theories that situate language in social and political landscapes. I describe a hybrid (quantitative/qualitative) approach to CMA as I apply it to a non-native speaker of English (NNSE) character from an internationally successful Hollywood film. I describe representations that “symbolically colonize” (Molina-Guzmán, 2010) the NNSE as lower class, lower status, and comfortably positioned as subordinate to his native-speaker counterparts. I then share examples of how students use CMA to further explore media cultivation of social attitudes toward language-learning, language policies, and NNSE identity. Overall, this article offers second-language teacher educators a theoretically informed model of analysis that engages TESL professionals as active participants in their media-saturated environments.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".