PELATIHAN PERENCANAAN BERBASIS DATA PADA PENGAWAS SEKOLAH, KEPALA SEKOLAH DAN GURU MENGGUNAKAN METODE INDENTIFIKASI, REFLEKSI DAN BENAHI (IRB) SECARA DARING
Bibliographic record
Abstract
Data-based planning training is carried out online based on various sources which can be used to carry out or prepare an agenda for future activities and budgets. The data-based planning training activity was carried out online for two days. This implementation started with the experience of the school principal sharing his experience with the education unit in planning and compiling activities and making budgets at the school, then the facilitator provided reinforcement for data mining from the independent teaching platform (PMM ), Education Report Card Platform as well as through data sourced from the North Sumatra Education Quality Assurance Center (LPMP). Then the facilitator reflects on the training on the material that has been explained. On the second day of implementation, each educational unit grouped to discuss and fill in the evaluation sheet, then carried out identification, reflection and improvement in planning activity plans and preparing budgets for the unit, then apart from that, the educational units held discussions to make follow-up plans starting from the date, unit involved and the media used. Next, reflect on your experience at school, then work on an evaluation worksheet that is synchronized with the Identification worksheet, then a reflection worksheet and a fix worksheet, then the education unit finishes working on an activity plan that comes from several data and can prepare a budget that is planned for the long term.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.024 |
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".