Bibliographic record
Abstract
This paper details my involvement as director of a media literacy program that brought together American And Nicaraguan youth to produce media about social issues. Grounded in civic engagement, youth leadership and media literacy, the program provided youth with media equipment and a series of workshops on digital literacy. Youth decided for their final project to re-create the colonial narrative Pocahontas. To me, this signaled a failure of critical media literacy programming to guide young people to tell critical stories. On further examination, I came to relate to this occurrence in a deeper way, wondering how they came to tell this story and discovering something rich and creative underneath the final product. In this paper, I explore the production process for this video, pushing at the boundaries of what constitutes both media literacy and civic engagement, and asking questions about how we understand what constitutes critical media literacy. Instead, I propose that when we focus on the product as what evidences critical literacy or civic engagement, we lose sight of the method. In this case, method was the home of powerful processes of literacy engagement around issues of class and race that were obscured by the use of the colonial narrative. This paper explores this tension, in order to both examine the challenges around producing a final product inextricably tied to colonial patterns of gender inequality and to give voice to the rich practices of critical literacy that the production process initiated.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.030 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".