Not All Who Want To, Can--Not All Who Can, Will: Extending Notions of Unconventional Doctoral Dissertations
Bibliographic record
Abstract
My research destabilizes imaginings of dissertations--conventional and otherwise--by highlighting a range of doctoral dissertations that, seemingly against all odds, manage to diverge from well-worn epistemic and textual paths. Whether it's a dissertation from South Africa whose author brings auto-ethnography and illness narratives into a discipline known for its skepticism of anything qualitative (Richards, 2012), a dissertation from Canada whose author purposely eschews standard edited academic English in order to privilege traditional Indigenous knowledges (Stewart, 2015), or a dissertation from the United States whose author coded and designed a digital scholarly edition of Ulysses without writing a single chapter in the process (Visconti, 2015), my research questions what brings these dissertations together while also considering what sets them apart. To reach a contextualised understanding of unconventional dissertations, including how they are produced and received, I adopt a textographic approach to the study of writing. Informed by this approach, as well as my stance towards writing overall, this dissertation draws on data that includes questionnaire responses, transcripts from unstructured interviews with writers of unconventional dissertations, and unconventional dissertations. Findings indicate that tendencies to conflate 'doctoral dissertations' with conceptions of legacy forms of scholarly communication still prevail. At the same time, the present study demonstrates how some dissertations may appear conventional on the surface to belie the unconventionality lurking below. Even entrenched forms of scholarship can shift when the functions and values motivating these forms are approached with open curiosity. Finally, while this study confirms widespread views that not all who want to create an unconventional dissertation will be able to, it also highlights some reasons for why those who can create unconventional dissertations may still choose to refrain. Framed as a response to urgent calls for critical examinations of how scholarly knowledge is produced, communicated, and assessed, this study contributes to a small but rapidly growing area of research that tracks 'unconventional' or 'non-traditional' scholarly projects and their lifecycles. Finally, this study also responds to collective needs for publicly accessible resources that can be used to advocate for diverse forms of scholarship and the equitable practices and infrastructures required to sustain them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".