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Record W4252055817 · doi:10.3138/chr.90.3.497

CHR Forum

2009· article· en· W4252055817 on OpenAlexvenueno aff
Terry Cook

Bibliographic record

VenueCanadian Historical Review · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)Identity (music)General partnershipPluralAgency (philosophy)Divergence (linguistics)HistorySubjectivityPublic historyPolitical scienceSociologyLawMedia studiesAestheticsArtEpistemologySocial scienceLinguistics

Abstract

fetched live from OpenAlex

Abstract: Historians and archivists approach the documentary past differently, as they consider, respectively, the ‘archive’ (singular) and ‘archives’ (plural). The former focuses on issues of power, memory, and identity centred upon the initial inscription of a document (or series of documents). The latter concentrates on the subsequent history of documents over time, including the many interventions by archivists (and others) that transform (and change) that original archive into archives. Despite making good common cause in lobbying over public policy, and initially sharing values based on objective, scientific history, the two professions have drifted apart in recent decades. This essay explores the reasons for this divergence by analyzing the history of the two professions and highlighting resulting misconceptions that blind both to deeper nuances of the multiple contexts surrounding records that may enhance their understanding and use. It concludes that archives are not unproblematic storehouses of records awaiting the historian, but active sites of agency and power. Until recently, it has been in the interests of both professions to deny (or at least not interrogate) the subjectivity of archives. Both professions could benefit significantly, therefore, from a renewed partnership centred upon the history of the record to produce better history.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.571
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.205
Teacher spread0.168 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2009
Admission routes1
Has abstractyes

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