Enhancing Foster Care Home NGO Sustainability via Social Franchising
Bibliographic record
Abstract
Research Question: This paper investigates how the social franchising approach may enhance the sustainability and capability of Foster Home NGOs in the Global South. Motivation: While many programmes exist to address issues such as poverty and lack of education for children in nations of the Global South, many operate in isolation, and are grassroots and/or stand-alone operations. Little research has been undertaken to understand how various approaches to organizational sustainability may be enacted for non-governmental organizations (NGOs) seeking to provide care for children in foster care homes. Our goal was to apply franchising and social franchising concepts as a framework for NGOs and non-profit organizations to use as a way of enhancing both the capability of achieving their mission as well as a method of organizational sustainability. Idea: Much of the literature on social franchising has been in the area of providing health care and services – however, this model may be useful to enhance the sustainability for NGOs and non-profit organizations that provide other critical services as well, such as foster care homes in the Global South. Findings: The social franchising model offers a concrete and actionable business model to foster home organizations with multiple homes to standardize care delivery as well as develop a strong core organization. Contribution: This paper explores how applying the social franchising model could enhance sustainability of NGOs with foster care home programmes, as well as some of the opportunities and challenges in applying this model to such NGOs and non-profit organizations.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".