Exploring the Relational Commitments of Negotiating Narrative Accounts in Narrative Inquiry
Bibliographic record
Abstract
The purpose of this article is to discuss and make clear the methodological commitments of co-composing and negotiating narrative accounts in narrative inquiry. The negotiation of interim texts is a widely used practice across a range of methodologies and paradigms (i.e., member checking, member validation, member reflections, and narrative accounting), yet there is little discussion on the varied philosophical underpinnings that shape the meaning and purpose of negotiating interim texts with participants. By drawing on the authors experiences of negotiating narrative accounts across projects this article will clarify the relational commitments that underpin this aspect of narrative inquiry as a move toward mutuality, co-composition, friendship, and ongoing attentiveness ordinary lived experience.
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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 | 0.104 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.105 |
| Scholarly communication | 0.029 | 0.047 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".