Economic Impact Assessment of Irrigation Development and Related Activities in Manitoba
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
Irrigation development is a capital-intensive process, which must compete with other uses for capital resources at the provincial level. In order to make a decision in favour of irrigation development, policy-makers must know if irrigation development is good only for the irrigators or is in the best interests of society as a whole, particularly in the rural Manitoba context. This study was undertaken to estimate the external (beyond irrigators) economic impacts of irrigation development. A regional input-output model, coupled with an employment model, was used for this estimation. All activities were broken down into those for the investment phase and those for the production phase. Investment phase activities bring forth economic impact only once, whereas those from the production phase are recurring in nature and last as long as the productive life of the capital assets. Results indicate that irrigation creates a significant amount of economic externalities in rural Manitoba: 7,349 jobs are created during the investment phase (about 735 per annum, assuming a 10-year adoption period), while during the production and processing phase 1,981 jobs per annum are created; 411 jobs at the farm level. Thus, for every job at the farm level, there are an additional 5.5 person-years of employment created during the investment, production and processing of irrigated products. Similarly in terms of gross domestic product, every hectare of irrigation generates an additional $10,680 worth of new wealth in the non-farm economy of Manitoba.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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