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Record W3006549320 · doi:10.5406/scanstud.92.1.0001

Curating and Performing Racism: Scenarios of Afrophobia in Contemporary Sweden

2020· article· en· W3006549320 on OpenAlexaff
Dirk Gindt, John Potvin

Bibliographic record

VenueScandinavian Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsConcordia University
Fundersnot available
KeywordsIconCitationDownloadPublishingRacismSearch engine optimizationLibrary scienceHistoryWorld Wide WebSociologyComputer scienceArtSearch enginePolitical scienceLiteratureLaw

Abstract

fetched live from OpenAlex

Research Article| April 01 2020 Curating and Performing Racism: Scenarios of Afrophobia in Contemporary Sweden Dirk Gindt; Dirk Gindt Stockholm University Search for other works by this author on: This Site Google John Potvin John Potvin Concordia University Search for other works by this author on: This Site Google Scandinavian Studies (2020) 92 (1): 1–38. https://doi.org/10.5406/scanstud.92.1.0001 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Dirk Gindt, John Potvin; Curating and Performing Racism: Scenarios of Afrophobia in Contemporary Sweden. Scandinavian Studies 1 January 2020; 92 (1): 1–38. doi: https://doi.org/10.5406/scanstud.92.1.0001 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressScandinavian Studies Search Advanced Search The text of this article is only available as a PDF. Copyright 2020 by the Board of Trustees of the University of Illinois2020 Article PDF first page preview Close Modal You do not currently have access to this content.

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)0.008
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0330.041
Scholarly communication0.0140.008
Open science0.0010.014
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.202
GPT teacher head0.395
Teacher spread0.192 · 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.

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

Citations2
Published2020
Admission routes1
Has abstractyes

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