World Anthropologies: Building Disciplinary Bridges and Upending Hierarchies of Knowledge
Bibliographic record
Abstract
This webinar brings together scholars involved in the World Anthropologies movement to discuss the current state of this vital project with global scope. Starting with the recognition that the building of anthropological knowledge is mainly constrained both by imperial/colonial structures and the default frame of the nation-state, the session will explore methods of doing anthropology otherwise. As an AAA sponsored event, the session will focus in particular on the role of the US and the AAA. We will reflect on the extent to which USian anthropology positions itself as an (or even sometimes the!) “anthropology without culture”, as well as on how USian institutions and structures produce a USian anthropology. Within the context of efforts from within the AAA and other national or multinational bodies (notably the World Council of Anthropological Associations) and supranational bodies (notably the International Union of Anthropological and Ethnological Sciences), we will discuss the struggle to decenter knowledge production from a place of privilege and domination. We will then consider how the World Anthro movement does or could build bridges with anthropologies that traverse, get around, subvert, or transcend “national” boundaries, and in particular the hegemony of the US, the AAA, and of English, and with movements that seek to queer anthropology, to decolonize it, to crip it, or to burn it down.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.001 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".