Bibliographic record
Abstract
Abstract Canada has abundant natural resources—its stock of natural capital wealth. A recurring debate in the literature is whether resource rich countries benefit in the form of higher sustained growth rates or not from the export of their natural resources. Canada's Harold Innis wrote extensively on this subject over 80 years ago and argued for the “no” side in the debate. Was he was right or wrong? I begin with the foundations of natural resource theory then turn to empirical work in recent decades. I agree with the literature that Canada overall has benefited from the export of its natural resources, but question whether that can continue given the focus on short term growth and the failure to account for the social costs of resource extraction and use—the environmental externalities that degrade and reduce stocks of natural capital. These externalities increasingly threaten our water and land resources and without more effective policy, the ability of resources to sustain growth and well‐being is questionable. Was Innis wrong? Yes in that the evidence supports the counter argument—resources have helped Canada become a developed economy with relatively high incomes and sustained growth rates. Innis was right that the uneven distribution of resources causes different impacts regionally especially during booms and busts and recognized the need to find substitutes for declining and degrading resource stocks. But Innis, like many after him, focused more on the intrinsic features of natural resources than policy to address the social costs of their development, a legacy that leaves us in a precarious position today.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".