COLLABORATION BETWEEN THE MINING INDUSTRY AND NGOs
Bibliographic record
Abstract
Can international development NGOs and the mining industry find common ground and work together in the Third World? The Canadian Hunger Foundation/PARTNERS in Rural Development (CHF-PARTNERS) has, over the past forty years, implemented more than 800 rural community development projects in some 38 countries of Africa, Asia, and the Americas. Working with or for the Canadian mining industry offers both opportunities and risks. We can help a mining company ensure that the workers, their families and communities share adequately in the economic and social rewards from a mining operation in their area. We can contribute to the provision of sustainable livelihoods when an operation winds down. The company’s reputation as a good corporate citizen can be enhanced. On the other hand, mining companies and NGOs have generally very different values and missions. The risk of misunderstandings from conflicting motives and work practices is correspondingly high. Both partners must state their values and expectations clearly at the start of collaboration. The NGO must ensure that the company abides by acceptable ethical standards. It must not directly or indirectly subsidize a private company from its charitable donations. The mining company must satisfy itself that the NGO has the capacity, resources and experience to produce the results expected. NGO/Mining Industry Collaboration
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".