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Record W4409523761 · doi:10.14195/2182-7974_38_1_1

Archives as instruments of power

2025· article· en· W4409523761 on OpenAlexfundno aff
Olivier Poncet

Bibliographic record

VenueBoletim do Arquivo da Universidade de Coimbra · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
FundersFederation for the Humanities and Social Sciences
KeywordsPower (physics)Computer sciencePhysics

Abstract

fetched live from OpenAlex

Considering archives as instruments of power, whatever that may be, with or without a question mark, is probably one of the most classic of all the facets of archives. Archives are associated with power and especially State power, even while power can take many forms, whether religious, economic, social, gender-based, etc., whether it is the power of one, the power of many, the power of all, whether it is sovereign, delegated or relative. We don’t have to consider power in a univocal mode, where it is necessarily confused with domination, force and constraint. Power administers, informs, protects and serves, just as much as it represses, controls, threatens or enslaves. It is power, its nature and objectives, that influence the value of archives as an instrument, and not the other way round, although the liberating and illuminating function of the written word remains secondary and ambiguous. It is possible to adopt a number of positions when considering the relationship between power and archives, whether this relationship is fundamental, instrumental or antagonistic. It could be summed up in a few simple formulas: power through archives, power over archives, power of archives. In short, the relationship between archives and power has three dimensions: functional, symbolic and critical. The social responsibility that the archivist has recently discovered and taken upon himself does not preclude the instrumental dimension of archives, nor does it eliminate their functional, symbolic or critical dimensions, but it does allow us to see more clearly what archives do for power or — to put it another way — what their power is.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.028
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0080.043
Scholarly communication0.0280.035
Open science0.0020.013
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0260.005

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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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