Gerakan Sosial dalam Aksi Penyelenggaraan Sekolah untuk Anak Miskin
Bibliographic record
Abstract
This article shows the importance of civil society in the implementation of a school for poor children that are seen in the context of the social structure and state policies. During this time, the implementation of the school for poor children—done by the civil society organization (CSO) in Indonesia—is often understood as a micro phenomenon regardless of the context of broader social structures. What they do in fact can not be separated from the context of the macro structure. To that end, the main argument of this paper using the framework of the concept of social movement that emphasizes the dynamics of CSO actors and providing education in the face of structural poverty problems that occur. In the early part, this paper outlines some of the dimensions of social movements in the implementation of the school for poor children that are characteristic of the CSO. Arguments will end with a description of the opportunities for the development and sustainability of the CSO movement. This article departs from the results of a qualitative research using case study as a strategy of inquriy. Data were collected through interviews, observation, and study of documents and analyzed with qualitative data analysis is enriched by an extensive literature study.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".