Dialogic narratives of literacy, teaching, and schooling: Preparing literacy teachers for diverse settings
Bibliographic record
Abstract
ABSTRACTS In this study, we focus on the “dialogic narratives” (Bakhtin, 1981, 1986) of selected preservice teachers within an innovative teacher education program in the Midwestern U.S. that included community‐based internships. In particular, we examine how these students author their identities as literacy teachers within the context of a mediated seminar setting. Drawing on Bakhtin's theory of discourse, we analyzed students' dialogic narratives as a way to understand the construction of their professional identities within particular discursive moments. Our analyses illustrate how the students negotiated authoritative and internally persuasive discourses as they authored their own narratives, revealing the complexity of preparing teachers to become flexible cultural practitioners in diverse settings. We argue that immersing students in community based environments and providing spaces for dialogue offer promising strategies for complicating and deepening preservice teachers' understandings of, and approaches to, language and literacy education in relation to issues of cultural diversity and social justice. [This article features supplementary online‐only material, including commentaries from Marilyn Cochran‐Smith and Allan Luke and Tara Goldstein, an annotated bibliography, and extensive data samples. See http:www.reading.orgLibraryRetrieve.cfmD10.1598RRQ.41.2.3&FRRQ‐41‐2‐Rogers‐supp_1.html .]
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".