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Record W4205318814 · doi:10.7202/1084741ar

“I Can’t Wait for You to Die”

2022· article· en· W4205318814 on OpenAlexvenueno aff
Harrison Apple

Bibliographic record

VenueArchivaria · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsForgettingQueerOral historyService (business)Archival scienceValue (mathematics)Field (mathematics)SociologyPublic relationsPolitical scienceLibrary scienceBusinessComputer scienceGender studies

Abstract

fetched live from OpenAlex

Stemming from conflicts over the authority of professional archives to arrange and steward community knowledge, this article outlines the limitations of the archival apparatus to produce the conditions for social liberation through acquisition and offers suggestions for how to operate otherwise, as a collaborator in forgetting. It discusses the origins and revised mission of the Pittsburgh Queer History Project (PQHP) as a reflection of the precarious definition of community archives within the discipline and field of archival science. By retracing the steps in the PQHP’s mission, as it moved from being a custodial and exhibit-focused collecting project to acting as a decentralized mobile preservation service, I argue that community archival practice is an important standpoint from which to critically reassess the capacity of institutional archives to create a more conscious and complete history through broader collecting. Specifically, I demonstrate how contemporary attention to the value of community records and community archives is frequently accompanied by a demand for such archives, records, and communities to confess precarity and submit to institutional recordkeeping practices.

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)0.007
metaresearch head score (Gemma)0.021
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0180.024
Scholarly communication0.0080.013
Open science0.0020.008
Research integrity0.0030.014
Insufficient payload (model declined to judge)0.0360.017

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 head0.209
Teacher spread0.176 · 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
Published2022
Admission routes1
Has abstractyes

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