Database of unconventional dissertations--Companion to Amell (2023)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p><span style="font-size:15px;color:#000000;font-weight:bold;text-decoration:none;font-family:''docs-Calibri'';font-style:normal;text-decoration-skip-ink:none;">What this is: </span><span style="font-size:15px;color:#000000;font-weight:normal;text-decoration:none;font-family:'docs-Calibri';font-style:normal;text-decoration-skip-ink:none;">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&#39; contexts. While I originally intended to host this spreadsheet using Google Drive, I&#39;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. </span></p>
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it