Economic Impact Assessment of Irrigation Development and Related Activities in Manitoba
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".