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Satisfaction Level of the Academic Community with the Implementation of WEB-Based SIAKAD IAKN Tarutung (Comparison: Implementation of SIAKAD UIN-Sumatera Utara)

2023· preprint· en· W4388573238 on OpenAlexaff
Sudirman Lase, David Fero, Apriliana Lase, Herlina Juni Risma Saragih

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldComputer Science
TopicBlockchain Technology in Education and Learning
Canadian institutionsTaylor College and Seminary
Fundersnot available
KeywordsAcademic communitySERVQUALWork (physics)Higher educationMedical educationValue (mathematics)PsychologyComputer scienceKnowledge managementBusinessService (business)EngineeringMarketingService qualityPolitical scienceLibrary scienceMedicine

Abstract

fetched live from OpenAlex

SIAKAD is a system developed to meet the needs of the academic community as a whole. Implementation of SIAKAD is based on three satisfaction indicators: access speed, ease of access and timeliness. Qualitative and quantitative methods on SERVQUAL analysis. Then analysed with statistical data as a comparison of satisfaction implementation in each academic community in IAKN and UIN universities. This study shows that the purpose of satisfaction affects the value of the academic community in providing an excellent and destructive impact on the value of work in higher education as well as the involvement of various elements in higher education to determine how the academic information system (SIAKAD) should be built to meet the expectations of its users and following SIAKAD standards in general in higher education.

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)0.002
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.246
GPT teacher head0.430
Teacher spread0.184 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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