Accommodating an Autoethnographic PhD: The Tale of the Thesis, the Viva Voce, and the Traditional Business School
Bibliographic record
Abstract
The authors explore how a traditional U.K. business school accommodated an autoethnography for a PhD thesis and the evaluation of the quality of innovative textual form. The authors argue that scholars can view the quality of an autoethnography in terms of two dimensions: the relationship with criteriology (consensus/dissensus) and context of quality judgment (focused on completed process/embedded in practice of completing). The authors further argue that the final judgment of thesis quality is shaped by the extent of match (or mismatch) between two dimensions: the awarding higher education institution’s (HEI) relationship with quality (i.e., its research traditions) and the context of quality judgment for the thesis (how the final thesis demonstrates quality). The authors show that supervisors and examiners are crucial for reinterpreting criteria to accommodate contemporary ethnography, particularly within traditional HEIs, and may exploit bounded discretion to do so.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".