Cementing Partnerships: Applying a Network-Mapping Tool in Tajikistan
Bibliographic record
Abstract
Background: This article describes the integration of an innovative network-mapping tool within a monitoring, evaluation and learning system. We describe how it serves to strengthen vulnerable families to care for their children. We discuss the use of this tool as part of the process of measurement for change in the preparation for the sustainable scaling of programme implementation. Tajikistan has a legacy of Soviet-style institutional care of children. Traditionally, very young children separated from their families have been cared for in institutional baby homes. This research is part of a wider project to transform these baby homes into community-oriented Family and Child Support Centres. Methods: We mapped the networks of child support experienced by parents and service providers. We used interactive, semi-structured interviews, and the tool evolved through an iterative process. We generated data to describe the connexions between children, families, professionals and supporting organisations. The resulting information revealed strengths and weaknesses in support provided, attitudes and perceptions towards the quality of the support as well as identifying processes through which changes strengthening the system can be stimulated. Results: The data showed that the main support for children comes from within their immediate household, but, over time, more distal support gained value. Variation in the networks of support related to gender, specific subgroups of need and location. Gender was the most influential determinant of patterns of support. Mothers' knowledge of service provision, represented by a greater number and variety of contacts on their network-maps, was more diverse than fathers'. In contrast, fathers' more limited networks showed connexions to individuals and organisations with potentially more powerful decision-making roles. Participation in the discussions around the network-mapping contributed towards a change in the use of data and evidence in the implementation team. Conclusions: Network-mapping is a valuable and adaptable tool that feeds into monitoring and evaluation at multiple levels. The process reveals the nature and extent of relationships of support for childcare and protection. It exposes the changes in these networks over time. Both the information provided and the process of collection can enrich care plans, create links within the network and inform decision-making that improves efficacy of delivery as we move to scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".