Bibliographic record
Abstract
Abstract During my graduate studies, I was encouraged by professors to do traditional forms of enquiry instead of exploring Arts-based Educational Research (ABER). As well, I found most people outside academia had heard the term ‘doctorate’ but few understood it. After completing my doctoral studies at the University of Glasgow, I created cathARTic (2012) as a way to explore ABER and share the personal and professional layers of that academic journey. It is a mixed-media work consisting of 216 scrapbook pages that may be displayed as a tapestry or in a series of binders. It is organized chronologically and built from my printed dissertation, research notes, journal entries, photographs and ephemera collected during my doctoral studies. This visual essay includes images of the work’s creation and related text, in an effort to show how I used Barone and Eisner’s seven features of ABER as a conceptual and logistical framework.
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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.038 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".