Bad Habitus: Anthropology in the Age of the Multimodal
Bibliographic record
Abstract
"The recent reframing of the Visual Anthropology section in American Anthropologist was motivated by a sense that new technologies have democratizing power and that through multimodal forms we can address a shift toward engagement and collaboration in anthropological research (Collins, Durington, and Gill 2017). Our purpose in this essay is to engage and expand the discussion raised by Samuel Collins, Matthew Durington, and Harjant Gill in their 2017 article 'Multimodal Anthropology: An Invitation,' which has been widely cited and has helped to inspire a range of new projects in anthropology that do not prioritize text. Although the idea of multimodal anthropology may challenge dominant paradigms of authorship, expertise, capacity, and language, we argue that there is nothing inherently liberatory about multimodal approaches in anthropology. Therefore, as our discipline(s) increasingly advocates for the multimodal in the service of anthropology, there is a need for deep engagement with the multimodal's position as an expression of technoscientific praxis, which is complicit in the reproduction of power hierarchies in the context of global capitalism, 'capital accumulation' (Collins, Durington, and Gill 2017, 144), and other forms of oppression."
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.063 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".