The palimpsest layers of pre-service teachers’ literacy autobiographies
Bibliographic record
Abstract
In this article, we examine three literacy autobiographies written by pre-service teachers. Narratives are seen as not just stories relating a set of facts, but rather a means by which individuals interpret their experience. Literacy autobiographies are a reflective and interpretive account of one’s development as a literate being. Using the tools of narrative analysis, we (a) examine these stories to understand the processes of literacy development through the experiences of learners’ storied lives; (b) seek to understand the impact that teachers’ literacy journeys have on their view of literacy and literacy education; and (c) explore what these literacy autobiographies reveal about the contributions of teacher reflection to pre-service teacher education. Our analysis points to the importance of personal relationships in the development of literacy, providing the context within which literacy practices give meaning to the literacy events active in the narrators’ lives. We also note a persistent view of traditional forms of literacy in contrast to pre-service teachers’ involvement in multiliteracies, and argue that this gap needs to be addressed in order to prepare teachers for the twenty-first century classroom. We also consider how reflection can be a more intentional aspect of pre-service teacher education to enhance pedagogy and learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".