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Record W4400894255 · doi:10.14421/fhrs.2023.181.58-71

STRATEGI LAYANAN DINAS PERPUSTAKAAN DAN KEARSIPAN KOTA DUMAI DALAM KEGIATAN STORYTELLING DI MASA NORMAL BARU

2024· article· en· W4400894255 on OpenAlexaff
Agung Hariadi, Rizca Defriyani

Bibliographic record

VenueFihris Jurnal Ilmu Perpustakaan dan Informasi · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBaruStorytellingComputer scienceArtPhilosophyTheology

Abstract

fetched live from OpenAlex

The library service is the backbone of the library. Without services, the collection of library materials cannot be optimally utilized. One of the services provided by the Library and Archives Department of Dumai City is storytelling services. Storytelling service is a service conducted to convey a story through words, pictures, photos, or sound. Challenges arising from the impact of the pandemic have resulted in the library services being less optimal. Nevertheless, the Library and Archives Department of Dumai City strategically conducts activities as a form of prime service to library users through innovative storytelling services. The method used in this research is qualitative descriptive. Data collection is done through observation and survey, including direct field monitoring and interviews with the research subjects. The research results state that the strategy and innovation of storytelling service activities during the pandemic are effectively carried out online by utilizing social media. When there are policies related to easing activities in the new normal, face-to-face interactions can provide a very interesting impression and receive a very positive response, even when adhering to health protocols.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.038
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0380.009

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 head0.025
GPT teacher head0.286
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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