Interacting Narratives and the Intentional Evolution of Personal Practical Knowledge: Experienced English Teachers' Multiliterate Innovations in the Professional Knowledge Ecosystem
Bibliographic record
Abstract
This study is an exploration of the lived experiences of three secondary teachers who have developed innovative approaches to English education in response to the needs of diverse, multi-literate urban students. The research marries multiliteracies pedagogy with narrative inquiry, and explores themes and discourses in the teachers’ narrations of their practices. From the new perspective developed from this pairing emerge two significant findings. First, the study contributes to teacher development by synthesizing concepts of design in multiliteracies pedagogy and personal practical knowledge in narrative inquiry. From this synthesis arises the notion of the intentional design of personal practical knowledge occurring through self-directed professional learning that leads to innovation in teaching. Second, the study develops the concepts of interacting narratives and professional knowledge landscape, offering a method of analyzing the multifaceted interactions of Self and Other narratives in the context of a professional knowledge ecosystem. This method provides a specific framework for contextualizing interacting narratives and provides a new clarity of focus in narrative research texts.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".