Economic Learning Media Development Based on Local Locality
Bibliographic record
Abstract
This study aims to describe the learning medium of economic education at senior High School in Banjarmasin with media based on local wisdom.This research uses qualitative method as developed by Miles & Huberman, starting from data collection, data reduction data display, and then made conclusion. Data were collected in the order of Basic Competence (KD) of Economics lesson, from Class X, XI, and XII. The data are grouped into media that are (a) audio, (b) visual, and (c) audio & visual. Respondents are economic teachers, MGMP Economics, the school, especially the principal in question.From this research, it is known that the use of learning media based on local wisdom in high school in Banjarmasin City has been done mostly using audio, visual or audio & visual media, in every Basic Competence (KD) of economic learning. Already there are inserted with the media based on local wisdom, but has not been explored to the fullest.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".