DAMPAK PEMBELAJARAN DARING MASA PANDEMI COVID 19 (STUDI KASUS PADA ORANG TUA PESERTA DIDIK)
Bibliographic record
Abstract
This study aims to know(i) Positif impact on learning of online pandemic COVID 19 for student parent who are working as farmers. (ii) Negatif impact on learning of online pandemic COVID 19 for student parent who are working as farmers. The research was classified as qualitative research with an case study methode. The instrument used on this study researcher becomes the key instrument. The data were collected using participants observations, interview and document studies. The data were analyzed using the Miles & Huberman model.Based on the result of data analysis in the concluded: (i) Positif impact on learning of online pandemic COVID 19 for student parent who are working as farmers is their children are able to learn online using mobile phones with various applications. (ii) Negative impact on learning of online pandemic COVID 19 for student parent who are working as farmers is the network issues, quotas, their children are as severely as pain as it is less than taking a lot of work from the teachers, their child rarely helps the parents at the house and in the garden because of learning time that is very solid.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".