Letting experience in at the front door and bringing theory through the back: Exploring the Pedagogical Possibilities of Situated Self-Narration in Teacher Education
Bibliographic record
Abstract
As a means of exploring what ‘learning through experience’ in teacher education might look like, situated self-narration is both conceptualized and performed here as the discursive practice through which already familiar and remembered experience may re-presented and re-organized from a forward-looking vantage point. Drawing on poststructuralist views of language and subjectivity and framed by a “pedagogy of possibility” (Simon, 1992 Simon, R. 1992. Teaching against the grain: Texts for a pedagogy of possibility, Westport, CN: Greenwood Publishing Group. [Google Scholar]), situated self-narration involves three main discursive strategies: interruption, interrogation and interpretation. By way of illustration, I use memory to interrupt my relationship to the dominant narrative of ‘English Teacher as avid reader’ and interrogate my everyday experiences of being a girl as mediated by popular culture, in both cases, drawing on a poststructuralist understanding of identity as an evolving constellation of discursive practices and foregrounding the distinctive qualities of one’s experiences as a possible source of agency. I consider the pedagogical possibilities of such identity work in the context of English teacher education, specifically in terms of teaching theory through the back door (Luke, 1993). I engage what it means to say that the way we “word the world” matters (St. Pierre, 2000 St. Pierre, E. 2000. Poststructural feminism in education: An overview. Qualitative Studies in Education, 13(5): 477–515. [Taylor & Francis Online] , [Google Scholar]) through my own interpreted experience as an evolving yet situated subjectivity; a consciousness-that-teaches.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.007 |
| 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".