Canada’s dependence on natural capital wealth: Was Innis wrong?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it