Unraveling Researcher Subjectivity Through Multivocality in Autoethnography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article analyzes and discusses the notion of including multivocality as an autoethnographic method to: (a) illustrate that there is no single and temporally-fixed voice that a researcher possesses, (b) unfix identity in a way that exposes the fluid nature of identity as it moves through particular contexts, and (c) deconstruct competing tensions within the autoethnographer as s/he connects the personal self to the social context. After providing a short, multivocal vignette based on the author's previous work assignment as a teacher educator in Kosovo, the author offers a reflective analysis of his approach. His analysis includes a critical discussion around the benefits and challenges of using such a method in autoethnography. The author concludes that research-oriented institutions might be resistant to validating multivocality as research practice given the myopic view that "voice" is linear, categorizable, and one-dimensional. In this way, the use of multivocality in autoethnography can also be understood as a way to liberate research practices from oppressive institutional rules and restrictions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it