Inventing expert in English language arts: A case study of critical literacies in a third grade classroom
Bibliographic record
Abstract
Drawing from data generated during the 2016-2017 academic year, this study centred on U.S. children’s design of two critical literacies compositions—a letter to Congress and a persuasive multimodal text. Situated within an integrated unit focused on (im)migrants, children asked legislators to act on the GOP Administration’s proposed border wall and the #MuslimBan. Simultaneously, their teacher took steps to engage students in critical literacies conversations about access in/to the United States. Using a case study design, I investigated the following: How might traditional perceptions of ‘expert’ shift as children engage in critical literacies using varied materials and technologies? Specifically, I highlight how, by engaging an expansive skill set of communicative practices, children designed texts and enacted identities related to civic agency. Through multimodal composing, one nine-year-old white boy exemplified how children highlight knowledge beyond what is captured in a written text. His multimodal response illuminated his deep understanding of the obstacles faced by (im)migrants as they traverse boundaries. To alleviate such challenges, he “invented” both a transportable water filter cup and a fishing tool and engaged in critical making. When provided with opportunities to compose multimodally, the child—a white boy marked as “behind” in literacy—demonstrated rich content knowledge not readily visible in his written responses. His compositions disrupted understandings of expert with regard to elementary writing and critical literacies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".