Bibliographic record
Abstract
Paved With Good Intentions provides a critical perspective on the negative impacts of Canadian Non-Governmental Organization (NGO) projects in relation to achieving the goals of equality and social justice in countries that are not considered to be part of the West (i.e.: Canada, the United States, or Western Europe), or what the authors refer to as the "Global South."Citing various studies from NGO specialists such as Sangeeta Kanat and Michael Edwards, James Petras, Eboe Hutchful, Mike Davis, Laura Macdonald, Tina Wallace, and Arundhati Roy, the authors situate their work within an anti/decolonial framework that seeks to achieve social justice from the bottom up.They examine the power of donors in controlling the direction and focus of development NGOs, which they contend reinforce colonial legacies of resource extraction and genocidal policies in the name of benevolence.The authors provide a nuanced yet perhaps contentious view of NGO work suggesting NGOs have become at best toothless critics which define the limits of dissent, and at worst proponents of Canadian imperial interventions.They distinguish between development NGOs and humanitarian NGOs, although they devote less space to the latter.In their view, humanitarian NGOs provide assistance for short periods of relief efforts such as during natural disasters, for example, while development NGOs try to alter the fundamental ways in which societies are organized.In their own words: "We consider development NGOs to be distinct from humanitarian agencies.Humanitarian action seeks to alleviate episodic instances of suffering, whereas development work seeks to address the root causes of poverty" (14).
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.034 | 0.009 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 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".