Knotty Tales: Canadian Staples and Post-Staples Forest Policy Narratives in an Era of Transition from Extractive to ‘Attractive’ Industries
Bibliographic record
Abstract
Political economists have typically understood the forest sector as part of the Canadian staples economy: early European settlers used forests for fuel, farming and construction purposes, and industry began later to cut raw timber and manufacture pulp and paper for export. According to the staples narrative, introduced by William Mackintosh and elaborated by Harold Innis, in order to settle the land and extract its resources, including forest products, colonists and settlers built an entire society and economy “organized around the labour force, technological regime, legal order, and financial system needed to serve the ends of resource extraction”. Building upon Innis’ work, a nationalist political economy school has criticized the domination of the Canadian resource economy by foreign capital, markets and technology, and advocated a ‘made-in-Canada’ industrial strategy. Studies on the forest sector have been especially prominent in probing the contingencies, specificities, and possibilities of building a forest policy that is more socially equitable, more value-added oriented, and more integrated into the national economy. More recently, however, many observers in the political economy and policy community tradition have noted a shift from an “extractive to an attractive model of development” within the forest sector, or what Hutton calls the “staples in decline syndrome”. Though he concedes that it is possible to overstate the staples in decline syndrome, he maintains that “we may be at the advent of a ‘post-staples’ state, in which resource extraction is essentially a residual of the national economic structure, a vestige of an historical development which sustained many Canadian regions”. In order to evaluate the extent to which Hutton’s observations ring true, this chapter grapples with divergent methods of approaching and analysing forestry.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.064 | 0.037 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".