Replikasi Kalkulator Deteksi Stunting dalam Program Gerabah Stunting Manis melalui Kolaborasi Pentahelix oleh DP2KBP3A Kabupaten Ciamis
Bibliographic record
Abstract
Introduction: Stunting remains a significant public health issue that requires a multisectoral and community-based approach. Local government initiatives supported by cross-sectoral collaboration are key to accelerating stunting reduction efforts, especially through the optimization of local wisdom and community empowerment. One of the strategic tools utilized in this program is the Stunting Detection Calculator (Kalkulating). Objective: This community service aimed to replicate the use of the Kalkulating platform in the implementation of the Gerabah Stunting Manis program through a pentahelix collaboration initiated by the DP2KBP3A of Ciamis Regency across five target sub-districts. Method: The program was carried out through district-level community dialogues (saresehan) from January 9–16, 2025, followed by village-level sessions from February 10–17, 2025, in five selected villages. The program included seven stages of mentoring and integrated Kalkulating for stunting risk screening in toddlers and adolescents. Data were collected through direct observation, screening results, interviews with stakeholders, and village reports. Result: The activity involved 63 community health volunteers (Posyandu cadres), 25 prospective brides and grooms, 114 pregnant women, 40 postpartum mothers, and 440 toddlers. Each village developed localized innovations as strategies to prevent stunting. Stakeholder collaboration was reflected in the involvement of various government agencies, academics, and the community. Conclusion: The replication of the Kalkulating platform in the Gerabah Stunting Manis program successfully strengthened early stunting risk detection while fostering the emergence of local innovations. The sustainability of this program requires continued cross-sectoral synergy, program integration, and active community participation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".