Children’s Rhetoric in an Era of (Im)Migration: Examining Critical Literacies Using a Cultural Rhetorics Orientation in the Elementary Classroom
Bibliographic record
Abstract
There is a particular urgency in this political moment to understand children’s experiences with current events. Drawing from data generated following the 2016 presidential election, this paper focuses on three racially and linguistically diverse children’s persuasive compositions. Within a critical literacies writing unit focused on (im)migrant experiences, children called on legislators to act on the Republican administration’s policies. Building on the understanding that all literacies are political and that teaching and learning are value-laden tasks, the author engaged a cultural rhetorics orientation—grounded in the understanding of texts, bodies, materials, and ideas as interconnected aspects of communication—for data generation and analysis. The findings highlight how children strategically employed rhetoric to persuade. They used logos, pathos, and ethos, as well as story, a central tool for meaning-making and building practices in the world. Ultimately, this study demonstrates how children, when properly supported, can agentively participate in critical literacies and act on real-world politics. Through the stories of young children, this study emphasizes what children have to tell adults and what a cultural rhetorics orientation, through its emphasis on story, enables literacies researchers and educators to understand about children’s composing.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".