Social Inclusive Education Project (SIEP) as a Community for Handling Children with Special Needs in Rural Areas
Bibliographic record
Abstract
Education is for everyone. This indicates that everyone deserves access to education either in urban areas or urban areas. The purpose of this study is to describe the handling of the Social Inclusive Education Project (SIEP) community for children with special needs in rural areas. Data collection techniques are used for observations and interviews with SIEP founders and volunteers. The results show that the SIEP community has carried out various treatments for special needs children in rural areas. An assessment was run by the volunteers before carried out the treatment. The handling is carried out after making the Individualized Education Program for each child with special needs, including down syndrome, motor barriers, specific learning difficulties, visual impairments, and speech delays. The children with special needs are given treatment according to the child's needs such as training to memorize the Qur’an, training in prayer procedures, the introduction of numbers and letters, training in pronunciation of vowels, reading storybooks, writing training, and swimming training. The effort and aid carried out by the SIEP community for children with special needs in rural areas are expected to be a reference for volunteering activities for children with special needs in Indonesia, Japan, Malaysia, Thailand, and other countries in the world.
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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 | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".