Narrative communication strategies: best practices for NGOs seeking funding from CIDA
Bibliographic record
Abstract
"In June 2010, at the G-8 Muskoka Initiative on Maternal, Newborn and Child Health, Prime Minister Stephen Harper promised that Canada would provide 2.85 billion dollars (over five years) in funding to support health initiatives for mothers and children in developing countries(Government of Canada, 2011).This focus on maternal and child health is in keeping with three of the eight Millennium Development Goals, which focus on empowering women, reducing child mortality and improving maternal health. AllUnited Nation member states have agreed to support these goals and achieve specified health, gender, environmental and educationaltargets by 2015(World Health Organization [WHO], 2011).Canada’s government has thus committed tremendous resources, both financial and administrative, to achieve these targets that relate to maternal and child health. Seventy-five million dollars of this promised money is being disseminated by CIDA (Canadian International Development Agency) to Canadian NGOs(Non-governmental organizations) who can provide program proposals that focus on maternal, newborn and child health initiatives in third world countries –most of the countries eligible for the funding are located in Africa(CIDA, 2010). It is from interest in the Muskoka initiative and the CIDA funding specifically that thisresearch project developed. When considering the relationship between NGOs and governmentas in this situation–and specifically in this case a Canadian NGO and the Canadian government –a few key questions arose. When producing proposals for government, specifically CIDA, what strategies do NGOs employ when communicating those proposals in the hopes that they will be allocated funds? How can these communication strategies be analyzed?" - p1
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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 | 0.100 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.032 | 0.017 |
| Scholarly communication | 0.027 | 0.024 |
| Open science | 0.008 | 0.034 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".