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Record W2780022303

Everything Old Is New Again: The Evolution of Generic Appraisal at Library and Archives Canada

2017· article· en· W2780022303 on OpenAlexaboutno aff
Jenna Murdock Smith

Bibliographic record

VenueArchivaria (Association of Canadian Archivists) · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsConceptualizationLibrary scienceRealisationPolitical scienceGovernment (linguistics)HumanitiesSociologyPublic administrationArtComputer sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

The concept of “generic appraisal” was part of the original conceptualization of macroappraisal as it was developed at the thenNational Archives of Canada (now Library and Archives Canada) in 1990. This hypothesis, that cross-institutional functional patterns and areas where institutions shared common characteristics could support its implementation, was never fully put into practice. In response to a new focus on recordkeeping in 2009, archivists developed tools based on common activities performed by many or all government institutions. This enabled a re-examination of generic appraisal in more depth. Over the next five years, generic appraisal grew to inform and fully support the new approach to government records disposition at Library and Archives Canada (LAC). This article provides an account of the evolution of generic appraisal at LAC, and of how it facilitated the redesign of a disposition program that puts macroappraisal theory into practice in a more effective, efficient, and accountable manner.RÉSUMÉLe concept de « l’évaluation générique » faisait partie de la conceptualisation d’origine de la macro-évaluation telle qu’elle a été développée aux anciennes Archives nationales du Canada (maintenant Bibliothèque et Archives Canada) en 1990. Cette hypothèse – que des courants fonctionnels pan-institutionnels et des domaines où des institutions partagent des caractéristiques communes peuvent appuyer sa réalisation – n’a jamais été entièrement mise en pratique. Lorsque l’accent a été placé sur la tenue de documents, en 2009, les archivistes ont développé des outils basés sur les activités communes réalisées par la plupart ou par toutes les institutions gouvernementales. Ceci a permis un réexamen plus approfondi de l’évaluation générique. Au courant des cinq années suivantes, l’évaluation générique s’est étendue pour influencer et appuyer pleinement la nouvelle approche de disposition des documents gouvernementaux à Bibliothèque et Archives Canada (BAC). Cet article fournit un compte rendu de l’évolution de l’évaluation générique à BAC, et de la façon dont elle a facilité la reconceptualisation du programme de disposition qui met la théorie de la macro-évaluation en pratique d’une manière plus efficace, efficiente et responsable.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

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)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.857

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.016
Science and technology studies0.0240.029
Scholarly communication0.0310.008
Open science0.0030.011
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0110.001

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 head0.013
GPT teacher head0.177
Teacher spread0.164 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Qualitative
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
Published2017
Admission routes1
Has abstractyes

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