Database of unconventional dissertations--Companion to Amell (2023)
Bibliographic record
Abstract
What this is: This is one source of data gathered between 2019 and 2021 as part of a broader doctoral dissertation research project on unconventional dissertations (Amell, 2023). In addition to collecting responses to questionnaire items and conducting interviews, I also collected and analysed 71 dissertations. Unconventional dissertations (n= 51) were identified via word of mouth, database searches, participants, a profile page on the Canadian Association for Graduate Studies (CAGS) blog, and/or via analysis. This database represents a snapshot of this effort. Fifty-one dissertations are listed. Each one offers an alternative take on what it means to be unconventional, depending on dissertators' contexts. While I originally intended to host this spreadsheet using Google Drive, I've since decided to upload it to a repository in favour of a more stable and public platform. Unfortunately this decision means that I will lose some of the more immediate interactivity that a platform like Google Drive can offer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.025 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.638 | 0.537 |
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".