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Record W4320726204 · doi:10.1177/17506980221150892

Memory as a means of governmentality

2023· article· en· W4320726204 on OpenAlexaboutno aff
Katrin Antweiler

Bibliographic record

VenueMemory Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsnot available
Fundersnot available
KeywordsGovernmentalityPoliticsNarrativeSociologyPolitics of memoryContext (archaeology)Perspective (graphical)The HolocaustHuman rightsGovernment (linguistics)EpistemologyPolitical scienceLawHistoryLiterature

Abstract

fetched live from OpenAlex

The article proposes to pursue Memory Studies as Studies of Governmentality. It aims to demonstrate how an analysis of memory, undertaken from the perspective of governmentality, can provide an important diagnostic of our time(s) and its political conditions, while furthermore illuminating how these are derived from narratives about the past. For this, I put into dialogue theoretical considerations of the Foucauldian concept with findings from a study of the Canadian Museum for Human Rights. With a special focus on public memories that are performed and produced at the juncture of Holocaust memory and advocacy for human rights, this article puts forward an innovative approach to public memory’s entanglements with contemporary politics and subsequently argues that any public memory can in the broader context of governmental rationalities be understood as a technique of government itself.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.056
Scholarly communication0.0100.012
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.095
GPT teacher head0.377
Teacher spread0.281 · 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 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

Citations9
Published2023
Admission routes1
Has abstractyes

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