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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 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 teacher head, 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".