Mostly True Confessions: Joint Meaning-Making about the Thesis Journey
Bibliographic record
Abstract
The thesis supervisory role is perhaps the most prominent, yet least understood, of a faculty member's teaching responsibilities. We retrospectively explore the doctoral supervisory experiences of a doctoral student and her thesis supervisor through the process of co-constructing a personal narrative of the journey. Our story addresses several assumptions of the thesis process: the dissertation is an original piece of research by the student; the supervisors in an arms-length relationship because the thesis is the intellectual property of the student; the supervisor and committee are experts while the student is a novice being introduced into the culture; the thesis process is the same regardless of the program and the goals of student and supervisor. We invite others to join our conversation by sharing stories of their experiences. By accumulating knowledge in an under-researched area, it is possible that higher education can improve its record of successfully completed doctoral dissertations.
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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 | 0.034 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.041 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".