Bibliographic record
Abstract
Abstract: Four years ago, I was invited to take on the position of managing editor for Art/Research International (ARI). As we celebrate the tenth anniversary of ARI, I seek to honour the voices that have informed my own engagement with arts-informed research. As an artful researcher, who is comparatively new to the academy, I wish to centre the importance of looking back in order to look forward. In this article, I slow down and trace my own steps. I honour the guidance that I received having Dr. Ardra Cole as my doctoral supervisor. I detail my process conceptualizing and defending an autoethnographic dissertation containing a collection of stories about coming out as queer later in life. I offer one of the stories from my dissertation, and reflect on how my own work was informed by the question posed by Pauline Sameshima and Carl Leggo (2013), “what does love have to do with education?” (p. 90).
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.061 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".