Researcher’s Autobiographical Narrative as a Tool Used in Reflective Research: Performative Autoethnography as Cognitive and Interpretive Challenge
Bibliographic record
Abstract
The article addresses the issue of understanding and intentional use of researcher’s autobiographical narrative in the social research on the example of performative autoethnography. The reference point to reflection is a research project conducted as part of an initiative titled “Microworlds of Maternity”. The research involved a researcher (the author of the research) telling and analyzing her own story in the form of a short video. The researcher’s autobiographical narrative is treated as a reflective research tool. Then, the so called external investigator (the author of this article) further analyzes the video material. Interpretation of the video provides insight not only into the way the researcher’s narrative is used in the research process but also into the way it is transformed into a visual story (knowledge about maternity experience). Such an approach provides an opportunity to explore the practice of joining together interpretation perspectives in the form of investigator triangulation and allows to present interactions between the narrative and its interpretation – in the context of the researcher herself and in the dialogue with the other investigator. As a result, performative autoethnography is presented both as a research approach and a space for multiple voice interpretation and reflection on the limits of cognition in the scientific research. Reflections presented herein focus on understanding the research process as a learning situation for: researchers, respondents and recipients. The example recalled in the paper is a study conducted in the area of educational 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 | 0.046 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".