Does Talk Lead to Action? An Examination of the Relationship between Debate and Funding for NGOs in Canada
Bibliographic record
Abstract
All organizations, including nongovernmental organizations (NGOs), need funding to survive and fulfill their mandates. What is the best strategy for securing that funding? Should groups work to attract government attention and be a focus of government debate or should they avoid this type of scrutiny? This article uses innovative data to systematically examine how being the subject of debate in parliament is related to NGO funding for Indigenous, women, and environmental groups. We also examine if the relationship between debate and funding is dependent on the political party in power. We use data collected from Canadian Public Accounts, which lists all grants to groups by the federal government, and the index of Hansard, a full record of parliamentary debates at the federal level in Canada. Our findings demonstrate that the relationship between debate and funding is dependent on the issue area. While debate is positively associated with funding in all areas, it is a stronger predictor of funding for environmental and Indigenous groups than for organizations focusing on women. In addition, the party in power is critical for shaping how debate is related to funding. Debate has a much stronger effect on environmental funding when Liberals are in power than it does when Conservatives control the Prime Minister's office. This research shows that NGOs must be strategic when garnering attention to their cause as more debate does not necessarily lead to more funding across issue areas and contexts.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".