Bibliographic record
Abstract
The staple theory was developed as an analytical framework to help explain the economic evolution of countries which have a relative abundance of land and natural resources, such as Canada. It can also be used to determine under what conditions staple-related economic activity positively contributes to economic development. Early staple theory maintained that the ability to overcome or take advantage of geographical factors was critical to bring staples to markets competitively. Regions can do little to effect demand, however having the capacity to reduce the unit costs of staples, such as transport cost, is critical to the development of staple exports. More recently, the introduction of linkages into analytical frameworks has been of particular importance. The extent and strength of linkages between staple products and the rest of the economy is important in determining the ultimate effect of staple exports on output growth. Even if staple exports constitute only a small percentage of GDP, staple production can still be a dominate force in the economy by way of its complex linkages. However some economies fail to develop potential linkages falling into the staple trap. Often these economies are underdeveloped or have an unequal distribution of income. Staple theory is useful in the explanation of the successes and failures in staple-related development. For an economy to be successful it must make important supply-side decisions and strengthen linkages within the economy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".