Bibliographic record
Abstract
Through the study of the concepts of voluntary work, community partnership, and extremism, this study aimed to find out to what extent it can involve the community in the effort to reduce extremism considering the legal, regulatory, and legislative framework together with the responsibility of individuals, institutions, and the State. This study identified the areas, forms, and importance of voluntary work as a measure of the advancement of communities. In this context, this work analyses the concept of community partnership, its importance, types, and participatory roles of individuals and institutions which get involved in voluntary work. This study also identified challenges voluntary work faces - related to institutional structure and practices - and how relevant it is for the security of the community and the reduction of extremism. The study’s most important result is that voluntary work can be complementary to the work of the State, as it includes not only the charitable and relief aspects but also the social, educational, health, environmental, and sports aspects. This study indicated that the commitment of voluntary workers to legal and regulatory frameworks makes voluntary work more regulated. The study concluded that the State’s responsibility toward volunteers is to legalize, organize, and control their contribution. The involvement of individuals and institutions is a good indicator of social responsibility. This study agreed with previous studies that emphasized the need to volunteer to improve societies by providing excellent public and private services.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".