2020 National Level Junior High School/Madrasah Library Competiton
Bibliographic record
Abstract
Background of the study: The National Library of Indonesia organizes competition for high school/madrasah libraries in the form of the High School/Madrasah Library Competition. In 2020, online competitions were organized due to the Covid-19 pandemic conditions. Purpose: The problem in this research is the point of view of the jury and the participants on the implementation of the High School/Madrasah Library Contest. Method: The research was conducted using a descriptive qualitative approach. The results showed that the top three winners of the competition from 2017-2020 were high school/madrasah libraries from the Yogyakarta, Riau, Central Java, East Java and East Kalimantan regions. Findings: The jury of the 2020 high school/madrasah library competition considered that the online competition was not good enough and the majority of the jury wanted the 2021 competition to be held offline. Meanwhile, the 2020 high school/madrasah library competition participants considered that the online competition was good and half of the participants wanted the 2021 competition to be held offline. However, the jury's opinion was in line with the participants that it should took longer time for the competition assessment process. Conclusion: The online competition is a solution to the Covid-19 pandemic, but the judges feel that they are unable to see the data needed to give an participants feel they are unable to show the evidence needed
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".