Outline of an interdisciplinary method: from counter-visual ethnography to tracing
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 visual essay shares ideas from a collaborative project that brings together visual art and social science research in a novel way. Though it has much in common with conventional approaches to ethnography, counter-visual ethnography is a unique approach to qualitative research in the social sciences insofar as it focuses on what is missing, removed, or absent from the world around us. Counter-visual analysis requires a process of investigating and reflecting on the research site (including visual representations of it) to excavate and re-interpret buried meanings or other relevant interpretations. We offer a guide to producing creative, artistic works out of counter-visual ethnography. The essay is structured chronologically, beginning with a short note on the social science starting point of this work. We open with reflections on our research in Uranium City, Saskatchewan, where this project began. We then outline the method going from counter-visual ethnography to palimpsest and tracing. This involves a collaborative, community-engaging approach to art and knowledge mobilisation that bridges traditional academic disciplines. We conclude with comments on additional ways to extend use of this technique and additional empirical sites where this interdisciplinary visual method could be applied.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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