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Record W4407147525 · doi:10.1177/14634996241303405

Theorizing ethnographically: No shares without acknowledgement

2025· article· en· W4407147525 on OpenAlexafffund
Tania Murray Li

Bibliographic record

VenueAnthropological Theory · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAcknowledgementEpistemologySociologyPhilosophyComputer science

Abstract

fetched live from OpenAlex

As anthropologists have long recognized, a share is not a gift. Shares belong to owners. Only someone acknowledged to be the owner of a share of valued goods is entitled to demand the portion that properly belongs to them. This insight invites further ethnographic research and theorizing. What kind of person is acknowledged to be the proper owner of a share? What are the conditions under which demands based on ownership are recognized or disallowed? Where acknowledgement is lacking, how can it be achieved? To address these questions, I draw on my ethnographic research in Indonesia. Indigenous highlanders I encountered in Sulawesi in the 1990s grounded ownership in an individual's labour. Contra popular assumptions about the naturally communitarian nature of Indigenous people, social membership in a kin group or community furnished scant grounds for sharing valued goods such as labour or food. Research I conducted with Pujo Semedi in 2010–2015 in Kalimantan's oil palm zone indicated that villagers whose land had been occupied by plantation corporations were convinced they were rightful owners of a share of plantation wealth. Yet, racial tropes inherited from the colonial era together with the plantations’ social, juridical and spatial arrangements impeded acknowledgement. Plantation corporations and their government allies saw no grounds on which to compensate villagers for their losses, include them in benefits or involve them in plantation affairs. Drawing upon these ethnographic sources and comparative material, I further theorize why there can be no sharing without acknowledgement.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0100.113
Scholarly communication0.0100.031
Open science0.0030.013
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.033
GPT teacher head0.389
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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