The Dissertation as Multi-Genre: Many Readers, Many Readings
Bibliographic record
Abstract
Since most academics have completed a dissertation, it is ironic that the genre is such an under-theorized, under-studied, and under-taught text (Rose & Mc- Clafferty, 2001; Lundell & Beach, 2002; Kamler & Thomson, 2006). Perhaps, like childbirth, it is best forgotten; more likely, as Bazerman’s comment above suggests, the linguistic and rhetorical complexities of the dissertation are simply inexpressible for most academics. Unfortunately, doctoral students are often in desperate need of help with their dissertations, and yet, when Kamler and Thomson (2006) searched the literature, they found a “relative scarcity of welltheorized material about doctoral supervision and writing” and remarked that “doctoral writing was a kind of present absence in the landscape of doctoral education. It was omething that everyone worried about, but about which there was too little systematic debate and discussion” (p. x). Our focus in this chapter is on the supervisory dyad and the collaborative relationship between doctoral students and their advisors. We see the dyad as a critical dynamic in the student’s apprenticeship in disciplinary consciousness, identity, and discourse, and we set out to discover what occurred in supervisory sessions, especially when writing was the topic.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".