Unraveling Researcher Subjectivity Through Multivocality in Autoethnography
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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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 | 0.052 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".