Bibliographic record
Abstract
Narrative is one of many qualitative methodologies that can be brought to bear in collecting and analysing data and reporting results, though it is not as frequently used as say in case studies. This article provides a window into its use, from the perspective of a researcher who has used it consistently over the past decade to examine early career researcher experience – doctoral students, and those who have completed their degrees and are advancing their careers. This experience has contributed to a robust understanding of the potential of narrative, as well as its limitations. This paper first lays out the broad landscape of narrative research and then makes transparent the thinking, processes and procedures involved in the ten-year narrative study including the potential for creativity that narrative invites. The goal is to engage other researchers to consider exploring the use of narrative – if it aligns with their epistemological stance.
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 | 0.072 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.061 |
| Scholarly communication | 0.026 | 0.050 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.016 | 0.026 |
| 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".