Narrative Research Evolving
Bibliographic record
Abstract
Narrative research methodology is evolving, and we contend that the notion of emergent design is vital if narrative inquiry (NI) is to continue flourishing in generating new knowledge. We situate the discussion within the narrative turn in qualitative research while drawing on experiences of conducting a longitudinal narrative study. The philosophical tensions encountered are described, as our understanding and application of narrative approaches evolved. We outline challenges in data collection and analysis in response to what we were learning and identify institutional barriers within ethics review processes that potentially impede emergent approaches. We conclude that researchers using NI can, and must, pursue unanticipated methodological changes when in the midst of conducting the inquiry. Understanding the benefits and institutional barriers to emergent aspects of design is discussed in this ever-maturing approach to qualitative research.
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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 | 0.145 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.031 | 0.049 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".