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Record W4392480776 · doi:10.7454/jipk.v24i1.004

Kesiagaan Menghadapi Bencana Pandemi Covid-19 di Kantor Arsip Universitas Indonesia

2022· article· id· W4392480776 on OpenAlexaff
Anggraeni, Novia, Anon Mirmani

Bibliographic record

VenueJurnal Ilmu Informasi Perpustakaan dan Kearsipan · 2022
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)VirologyMedicine

Abstract

fetched live from OpenAlex

The condition of restrictions on community activities due to the COVID-19 pandemic has forced the Archives Office at Universities to adjust archive services. COVID-19 disaster preparedness needs to be implemented within the University of Indonesia Archives Office. This study aims to identify the preparedness of the University of Indonesia Archives Office in dealing with the COVID-19 disaster and. This is qualitative research with case study method. The results show that the University of Indonesia Archives Office has responded to the COVID-19 pandemic situation by carrying out various preparedness efforts that are implemented in service activities and archive management. The obstacle faced by staff and leaders during the pandemic is establishing communication and interaction. Based on the results of the study, the suggestion from this research is that the University of Indonesia Archives Office needs to make a post-disaster recovery plan and look for efforts to establish effective communication during the pandemic between staff and leaders.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.005

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 head0.021
GPT teacher head0.235
Teacher spread0.214 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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