Native Informant as Impossible Perspective: Information, Subalternist Deconstruction and Ethnographies of Globalization*
Bibliographic record
Abstract
La connaissance produite au moyen de L'ethnographie est longtemps entrée dans la culture populaire, et la propagation de stéréotypes et d'informations à propos de différences culturelles est un trait caractéristique de notre époque. Jusquà présent, L'intégration de L'ethno‐graphie dans les circuits de L'information de la culture postmoderne a peu retenu L'attention de la critique. Comment la recherche ethno‐graphique sur la mondialisation peut‐elle s'opposer à une réduction formelle des idées dans le monde de L'information ? Cet article repense la pratique de la théoricienne littéraire Gayatri Spivak selon laquelle la «déconstruction subalterniste« est une stratégie critique d'appréhension de L'ethnographie. Cette déconstruction ouvre la porte à une politique matérialiste de la forme en assistant à la fiction implicite dans la métaphore du texte social. Knowledge produced through ethnography has long flowed into popular culture, and the circulation of stereotypes and information about cultural differences is a characteristic feature of our time. Yet the integration of ethnography into the circuits of information of postmodern culture has received little critical attention. How can ethnographic research on globalization resist the formal reduction of ideas into information? This essay reworks literary theorist Gayatri Spivak's practice of subalternist deconstruction as a critical strategy of reading ethnography. Subalternist deconstruction enables a materialist politics of form by attending to the fiction implicit in the metaphor of the social text.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".