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Record W1787189438

Moments of Risk: Identifying Threats to Electronic Records

2007· article· en· W1787189438 on OpenAlexvenueno aff
David Bearman

Bibliographic record

VenueArchivaria · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt
DOInot available

Abstract

fetched live from OpenAlex

The author left the field of electronic records a decade ago, and has returned to survey the landscape of what at that time appeared to be a battleground of irreconcilable positions. Since 1997, significant areas of agreement seem to have emerged. This paper identifies six agreed “moments of risk,” which occur at critical state transitions in the life of records: at capture, maintenance, ingestion, access, disposal, and preservation. It examines the literature of the past decade to identify the commonly held criteria by which records can be known to have survived such moments of risk unscathed. By locating widely accepted critical points in the life of records and the criteria by which we can assure ourselves that our management methods have succeeded, it hopes to make way for tests that could be agreed between proponents of different strategies. RÉSUMÉL’auteur a quitté le domaine des documents électroniques il y a dix ans, puis il y est retourné afin d’examiner ce qui à l’époque semblait être un champ de bataille de positions irréconciliables. Depuis 1997, des points de convergence significatifs semblent être ressortis. Ce texte identifie six « moments de risque » qui se produisent lors de transitions d’états critiques dans la vie des documents : l’enregistrement, le maintien, l’ingestion, l’accès, la disposition et la préservation. Il examine la littérature de la dernière décennie dans le but d’identifier les critères généralement reconnus selon lesquels on peut savoir que les documents ont réussi à traverser ces moments de risqué indemnes. En repérant les points critiques dans la vie des documents et les critères selon lesquels nous pouvons nous assurer que nos méthodes de gestion ont réussi, il espère préparer l’arrivée de tests qui pourraient être acceptés par les défenseurs de différentes stratégies.

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)0.011
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.097
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0080.005
Science and technology studies0.0050.005
Scholarly communication0.0120.014
Open science0.0020.009
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.033
GPT teacher head0.253
Teacher spread0.220 · 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 designQualitative
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

Citations25
Published2007
Admission routes1
Has abstractyes

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