Foreign aid, the mining sector and democratic ownership: The case of Canadian assistance to Peru
Bibliographic record
Abstract
Abstract Background As foreign aid donors are increasingly open about seeking to obtain benefits from their development assistance, new forms of donor‐driven private‐sector partnerships have proliferated. This new trend is especially controversial in the mining sector, to which Canada has become the largest aid donor among OECD‐DAC countries. Purpose In order to better understand this phenomenon and its implications, this article asks, first, how has aid to the mining sector evolved and what do the changes suggest about its underlying motives? Second, what are the implications regarding the “democratic ownership” of the recipients’ development agenda? Approach and methods The study analyses Canadian aid to the mining sector in Peru, its largest recipient of such aid, concentrating on the period since 2011, when Canadian aid took an “extractive turn.” It draws on 20 semi‐structured interviews with key players and observers in Lima and Cusco in Peru, as well as an in‐depth review of mainly secondary sources and some statistical data. Its analytical framework is based on the motives that must underpin aid, as stipulated by Canadian legislation, and the concept of “ownership,” the cornerstone of the international aid effectiveness agenda. Findings The extractive turn in Canadian aid reflects an increase in commercial self‐interest, at the expense of altruistic poverty reduction and contradicting core elements of the legislated mandate of Canadian aid. Extractive‐related aid to Peru now almost exclusively supports: (a) strengthening the central government’s role in promoting mining; (b) encouraging municipalities to negotiate mutually beneficial relations with Canadian mining companies; and (c) subsidizing Canadian companies’ efforts to obtain a “social licence to operate” from local communities. Canada’s assistance to the mining sector can be justified by a narrow interpretation of the concept of country “ownership.” However, its justification rests on a limited vision of ownership, based on what governments, who claim to speak on behalf of citizens, prioritize, rather than a more democratic conception that takes into account what poor people want, which may include or preclude mining activities. Policy implications Aid donors should focus on locally owned strategies that reflect poor people’s priorities, independently of whether they include or exclude allowing mining companies to operate on their territories. Aid may thus contribute to a donor’s commercial interests, but the latter should not be the underlying motive.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".