Making sense of the public PhD dissertation defense: a qualitative multi-case study of education students' experiences
Bibliographic record
Abstract
A public oral defense of the written dissertation is mandatory for completion of the doctorate in most North American universities, yet how students experience it has rarely been documented.This study examined how a group of Canadian education PhD candidates experienced and made sense of their dissertation defenses.Employing an identity lens, it focused on how these students viewed themselves, performed, and were viewed by others as researchers before, during and after the defense.The perspective on identity was principally drawn from the communities of practice (COP) theory.Accordingly, doctoral candidates' researcher identities were defined by memberships (being members in the community of researchers in certain fields/areas of research), meanings (making sense of defense experiences) and trajectories (ways in which the defense connects doctoral candidates' past, present, and future).The participants were 11 PhD candidates (six women and five men) from three departments in a faculty of education at a Canadian research-intensive university.Each of them was interviewed before the defense about his/her preparation experience and after the defense about his/her defense experience.Through observation, questions from the 11 defense committees and the candidates' answers were recorded.Data also included the participants' background information and institutional documents regarding the conduct of the PhD dissertation defense.Over 20 other defenses were observed to understand the Faculty practices associated with the defense.Case-specific findings pointed to how doctoral candidates performed as researchers during the defense by balancing knowing and not-knowing in answering defense committees' questions.The candidates navigated across research areas, methodological and epistemological borders, and drew on various sources of knowledge to demonstrate knowing; and they evaluated the significance and relevance of questions and provided provisional and hedged answers to negotiate not-knowing.Cross-case findings indicated that the defense confirmed most candidates' researcher identities and played a role in weaving together their past, present and future in terms of their researcher identity development.The study concluded with implications for interpreting doctoral candidates' defense experiences and for understanding the functions of the public PhD dissertation defense.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".