Bibliographic record
Abstract
Abstract Since the 2010s, there has been an enthusiastic call for using multimodal methods in anthropological research. This call dovetails with the democratization of media production technologies that make the collection of data and the distribution of findings more accessible, particularly to audiences outside of academia. Existing modalities, such as sound, image, print, and archival practices, are joined by newer digital modalities, including social media, digital mapping, video games, and artificial intelligence technologies. The contemporary field of multimodal anthropology has developed from longer-established fields of visual anthropology and media anthropology, which have participated in the production, dissemination, and study of images since the advent of the camera. Anthropologists were among the first to use camera equipment and sound recorders in their research activities when these were considered new technologies in the early 20th century. Together, anthropologists and experimental filmmakers developed the tradition of ethnographic filmmaking, a cinematic genre that for decades was nearly synonymous with the field of visual anthropology, a subdiscipline including soundscape production. Anthropologists have explored multimodality as a feminist and decolonial approach to ethnographic methods. Newer multimodal methods strive to redefine and expand what counts as knowledge in ways that move beyond racist, colonial, and ableist legacies in the field of anthropology. Like the field of media anthropology, multimodal anthropology acknowledges and embraces the central role that media play in everyday life, for anthropologists and interlocutors alike. Notably, the initial excitement for multimodal methods has been closely followed by an ambivalence among scholars, who account for how new digital media tools often fall short of creating the hoped-for social and political change. The ethical use of new online platforms requires scholars to remain cautious of the ways in which popular digital technologies are often subsumed in contemporary forms of racial capitalism and White supremacy through ongoing issues of data-centered extraction and exploitation. Scholars working with multimodal methods are called to embrace the potential of this new field while being aware of its limitations. Such awareness requires scholars to center the contributions of intersectional feminist and decolonial approaches to doing multimodal anthropology.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 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".