MANAJEMEN PEMBELAJARAN TEMATIK INTEGRATIF SESUAI KURIKULUM 2013 PADA KELAS 4 SD KHADIJAH SURABAYA
Bibliographic record
Abstract
The purpose of this study is to describe the implementation of management of integrative thematic learning based on curriculum 2013 at grade 4 khadijah primary school Surabaya. The focus of this study are 1. The lesson plan for integrative thematic class based on curriculum 2013 at grade 4 khadijah primary school . the research method of this study is descriptive kualitatif, the data collection technique are interview, observation and documentation. The result of this study are 1) the lesson plan of integrative thematic based on curriculum 2013 at grade 4 khadijah primary school Surabaya conducted by several steps are : a) set thema, b) doing analisys SKL, KI, and basic competence, c) arrage syllabus, d) arrage the lesson plan, 2) doing integrative thematic learning based on curriculum 2013 at grade 4 Khadijah primary school used scientific approch by observing, questioning, reasoning, trying, processing, displaying, verivicaying, and communicating, 3) the assessment of integrative thematic learning based on curriculum 2013 at grade 4 khadijah primary school used authentic assessment that include written assessment, project assessment and portfolio assessment.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".