Embracing new paths in visual research facilitation: opportunities, tensions & ethical considerations
Bibliographic record
Abstract
This introduction to the special section establishes facilitation as an important yet underreported component of visual sociological research. Although institutional and regulatory ethics have been ingrained in university research settings, scholars such as Eve Tuck and K. Wayne Yang (2014) have asked us to consider the ways in which participants’ and communities’ refuse research influence our ethical frameworks. We take up Tuck and Yang’s call to ask: What does ethical research facilitation look like beyond institutional guidelines in visual research? What might deep, ethical, meaningful or useful facilitation look like in visual studies? Putting social justice concerns about power within research processes at the fore, the editorial argues that thinking through research facilitation must go beyond researcher reflexivity, and move towards action within the research settings in which we work.
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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 | 0.157 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.066 |
| Scholarly communication | 0.036 | 0.033 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.024 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 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".