Bibliographic record
Abstract
An abundance of natural resources might seem like something any nation would want to be blessed with. But in some countries, a bounty of energy, minerals and other resources can become as much a curse as a blessing. The difference between whether resources benefit a country’s people, or lead to adversity and even suffering, has everything to do with how a country manages its resources. It is the difference between a resource-rich, free and democratically accountable country, such as Canada, and a resourcerich, corrupt, violent and impoverished country, such as the Democratic Republic of Congo. In many resource-rich countries, the effect of ample natural wealth has been to sever the accountability link between citizens and government, slowing or even reversing democratic and social progress, while mostly enriching a few politically favoured constituencies. Canada’s plentiful resources are an indisputable blessing, and those critics of federal industrial policy who compare this country to illiberal and corrupt “petro-states” are being either ignorant or deceitful. There are numerous critical factors at work here that ensure that the Canadian public benefits, rather than suffers, from our natural endowments. We have a diversity of resources, as opposed to being reliant on a single commodity, and our natural-resource sector makes up only a small portion of our larger economy. We have well-established and diligently enforced standards for financial transparency and accountability, in both the private and public sectors. But, just as importantly, there is a national consensus in Canada that public wealth amassed from resource rents should be invested in strengthening human capital, through education, training and social services, as well as in improved infrastructure and better governance, eventually parlaying natural-resource wealth into a yet larger, further-diversified economy. But Canada — and especially resource-rich provinces, such as Alberta — cannot take these factors for granted. A combination of complacency and natural wealth has the potential to turn a blessing into a curse. Even once reasonably democratic and accountable countries, such as Venezuela, have been caught unprepared on the dangerous double edge of a resource boom and have seen their governance systems substantially eroded. Developing the fiscal capacity to withstand commodity-market shocks, creating effective and durable checks and balances on systems of legislative power, enforcing transparency in budgeting and public-investment management, and maximizing tax efficiencies and tax administration, are all areas where Canadians can and should focus their efforts. These are the fundamental safeguards that will ensure our ample natural resources continue to be seen by our citizens as a blessing and not — as is the unfortunate case in so many other countries — a curse.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.023 | 0.016 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".