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Record W2961690564 · doi:10.1007/s10502-019-09317-9

Archives in a changing climate: proposing new “solutions” for a new era

2019· article· en· W2961690564 on OpenAlexaffabout
Viviane Frings‐Hessami, Fiorella Foscarini

Bibliographic record

VenueArchival Science · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCultural heritageHistoryClimate changeEnvironmental ethicsEnvironmental resource managementArchaeologyEnvironmental scienceGeologyOceanographyPhilosophy

Abstract

fetched live from OpenAlex

The International Conference on the History of Records and Archives (I-CHORA) has been held since 2003 as a periodic conference aimed at encouraging and promoting interdisciplinary research into the history of records, recordkeeping practices and recordkeeping institutions.Conferences to date have taken place in Toronto (2003), Amsterdam (2005 and2015), Boston (2007), Perth (2008), London (2010) and Austin (2012), each attracting between 100 and 130 scholars and professionals from around the world (Foscarini et al. 2016, pp.xi-v).The eighth iteration, I-CHORA 8, was held at Monash University in Melbourne on 28-30 May 2018.The theme of the conference was "Archives in a Changing Climate" (http://ichor a.org/).Participants discussed the multiplicity of historical contexts in which archives are created, transmitted, preserved and reactivated, and the fast pace of change to which contemporary recordkeepers have to adjust to keep up with new technologies and new expectations.In the first of two special issues of Archival Science dedicated to papers from I-CHORA 8, we present four articles that propose new "solutions", or better, aspirations, for a new era in archives and recordkeeping.The first contribution by Jessica Lapp suggests a way of rethinking the role played by archivists, whereas the other three articles, by Cate O'Neill, Viviane Frings-Hessami, and Sue McKemmish, Tom Chandler and Shannon Faulkhead, offer ways of reframing recordkeeping practices that focus on the rights of the subjects of the records.

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)0.024
metaresearch head score (Gemma)0.025
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: none
Teacher disagreement score0.031
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0120.031
Scholarly communication0.0310.066
Open science0.0050.018
Research integrity0.0140.015
Insufficient payload (model declined to judge)0.0160.004

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 head0.232
Teacher spread0.195 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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