Stepping up to the plate: Climate change, faith communities and effective environmental advocacy in Canada
Bibliographic record
Abstract
There is widespread agreement that the mobilization and coordination of faithinspired actors is essential to maximize the impact of aid and development agencies worldwide (Bergmann 2009; Spencer, White and Vroblesky 2009; World Bank 2011b). Empirical evidence suggests that, in some countries, faith-based organizations (FBOs) provide a large share of the education and health services used by the local population (World Bank 2011c). Over the past decade partnerships have been developed between major development agencies and FBOs. Such assistance is all the more essential in light of climate change, which threatens to undo years of development work undertaken by humanitarian non-governmental organizations (NGOs) (McGray, Hammill and Bradley 2007; World Bank 2009). Climate change makes the vulnerable even more vulnerable, threatening not only livelihoods but more specifically the security and stability of the food systems on which they depend (FAO 2008). Changing precipitation patterns, floods, droughts, increased intensity of storms, sea level rise, and changing disease patterns are already reducing the availability of drinking water, the health and well-being of children, and physical safety among those vulnerable to disaster. Such changes may become even more challenging in the future if dire predictions about “tipping points” hold true (Stern 2007; World Bank 2009).1 Such problems will be particularly severe for those groups that development agencies have long targeted: those with less capacity to adapt, such as those who live in poverty in the Global South (Füssel 2009; IPCC 2007; Lobell et al. 2008; Toulmin 2009). This has led to FBOs being active in both climate change advocacy and project implementation (Mitchell and Tanner 2006; Parris et al. 2009; World Bank 2011a), alongside other development aid agencies and NGOs (McGray, Hammill and Bradley 2007; Mitchell and Tanner 2006; Mitchell and van Aalst 2008; Parris, Lansley and Finnigan 2009).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".