Literacy Narratives for 21st Century Curriculum Making: The 3Rs to Excavate Diverse Issues in Education
Bibliographic record
Abstract
Abstract This chapter explores literacy narratives as a narrative inquiry approach used in a Canadian education foundation course which focuses on story and experience as told and retold through letter-writing correspondence among teacher candidates. The process is illustrated in the chapter through a literacy narrative exemplar. The 3R framework developed by the author in her research program on poverty and education was applied to teacher candidates’ narrative ways of excavating storied experiences and assumptions in schooling. The 3R framework helps teacher candidates deconstruct their literacy narrative correspondences in order to avoid ‘hardening’ into their lived storied experiences as they work through the framework of: narrative reveal to help them excavate unconscious assumptions that surface in their writing; narrative revelation to show how they can interrogate further their own (sometimes biased) experiences, and; narrative reformation to show how prospective teachers can begin to transform teacher knowledge through awakened new narratives. Literacy narratives, as a curriculum making pedagogy to deconstruct formally and informally using personal educative experiences, readings from the course, and usage of the 3R framework, is a pedagogical example of social justice that gives dignity, respect, and perspective in order to reframe thinking about diverse issues in teaching and teacher education.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".