Bibliographic record
Abstract
In the 2013/14 budget, the Minister of Finance made a surprise announcement: the decision to merge CIDA with the (renamed) Department of Foreign Affairs, Trade and Development. A sensible move? If the intent is to link CIDA projects to Canadian resource developments overseas, probably no. If the intent is to respond to widespread criticism of CIDA’s past performance – by the Auditor General among others – probably yes. In a seemingly unrelated event, in April 2013 a garment factory collapsed near Dhaka killing over 1100. This event highlighted the poor level of governance exercised by the government of Bangladesh over safety standards. There is a prominent Canadian connection to this tragedy. Loblaw Companies Ltd. buys most of its Joe Fresh stock from Bangladesh – and some items came from the collapsed factory. Major importers in Europe and North America have decided the status quo is no longer viable. Loblaw has joined other major importers in accepting financial and supervisory responsibility for factory remediation. This is a major, if belated, exercise in corporate social responsibility. Bangladesh has for decades been a “country of focus” for Canadian aid. Beyond that fact, the link between these two decisions is the problem of “weak governance” in many low-income countries, Bangladesh included. How can aid be made to work in countries with weak governance? How can firms engage in trade with such countries in a responsible way? The Commentary discusses several tactics for more effective aid delivery and the potential for corporate social responsibility in a context of weak host country governance. The potential for a “win-win” outcome from Canadian investment in developing countries is much greater in the manufacturing than the resource sector. The Commentary also includes an Appendix on recent trends in development economics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.035 | 0.027 |
| 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".