Using Narrative Inquiry to Inform and Guide our (Re) Interpretations of lived experience
Bibliographic record
Abstract
ABSTRACT. In this paper, I share stories that provide sites of inquiry for the (re)interpretation of my own educative experience. Crossing time, I (re)visit and (re)construct seminal events in my life using knowledge gleaned in the intervening years to come to see how these life stories inform and guide me in the present. I use my own stories to enhance my understanding of the direction I am taking in my life since they are what I know, and I offer them here as an example of how narrative inquiry can provide a theoretical and practical framework for (re)interpreting our lived experience. RECHERCHE NARRATIVE COMME OUTIL DE DOCUMENTATION ET D’ORIENTATION DE NOTRE (RE)INTERPRETATION D’EXPERIENCES VECUES RESUME. Dans ce document, je raconte des recits qui sont sources d’interrogation pour l’(la re)interpretation de ma propre experience de l’enseignement. Je (re)visite et (re)construit les evenements fondamentaux de ma vie en faisant appel aux connaissances glanees au fil des ans pour en arriver a voir comment ces recits me renseignent et m’orientent aujourd’hui. Je relate ma propre experience pour mieux comprendre la direction que je donne a ma vie puisque c’est ce que je connais, et je la cite ici pour montrer comment une recherche narrative peut constituer une structure theorique et pratique pour interpreter ou reinterpreter notre vecu.
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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 | 0.061 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.013 | 0.053 |
| Scholarly communication | 0.027 | 0.036 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| 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".