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Record W4388856436 · doi:10.3390/humans3040022

Talking about Difference: Cross-Cultural Comparison and Prejudice in Anthropology and Beyond

2023· article· en· W4388856436 on OpenAlexafffund
Bob W. White, Mathilde Gouin-Bonenfant, Anthony Grégoire

Bibliographic record

VenueHumans · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversité LavalUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPrejudice (legal term)SociologyPerceptionEpistemologyCultural anthropologyContext (archaeology)Cultural diversitySocial scienceAnthropologySocial psychologyPsychologyGeography

Abstract

fetched live from OpenAlex

In recent years, the question of “difference” has become a central feature of public debate and social concern, especially in the context of transnational migration. The underlying question that we attempt to answer in this article is: how can we talk about difference without reinforcing prejudice? Starting from the observation that perceptions and representations of difference have an impact on the way that individuals and groups interact with each other in increasingly diverse urban environments, we argue that a systemic approach to the analysis of intercultural situations gives us a unique window into emerging discourses and evolving norms about difference. After a brief historical overview of debates surrounding cross-cultural comparison in anthropology, we consider how various fields outside of anthropology have drawn inspiration from anthropology in order to gain a deeper understanding of intercultural dynamics in various professional settings. This article also examines several anthropological concepts that have been used as tools to theorize cross-cultural comparison, and how participants in a new research methodology use the systemic notion of “cultural variables” to resolve the basic paradox underlying pluralist philosophy and practice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.406
Teacher spread0.367 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2023
Admission routes2
Has abstractyes

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