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
This article documents how I came to combine autoethnographic accounting with visual arts practice. I developed this mixed methods approach for my PhD study which explores the interdisciplinary possibilities offered by combining visual arts practice with STEM (science, technology, engineering, and mathematics). Visual arts practices as narrative forms tend toward the non-linear (Anae, 2014), whilst autoethnography offers self-reflection. Writing an autoethnographic account for an artwork has the potential to generate a wealth of data, some of which are visible, some of which are not. The invisible data become available only when the artist speaks to/writes about the artwork. If some content/context of a visual artwork is only visible through background information provided by the art maker, this discovery troubles another issue concerning our notions of what a good visual artwork is. Finally, I test this article’s autoethnographic authenticity against Adam’s four characteristics of autoethnography.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.001 |
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