Effects on Competitiveness of Government Interventions in the Agri-Food Sector in Canada and the United States [A Conceptual Framework]
Bibliographic record
Abstract
The scope of this project was limited specifically to addressing the impacts of government policies on competitiveness.It must be recognized that competitiveness is only one of a number of goals that should be considered in policy development for the agri-food sector.Many other socio-economic factors must be considered in the development of a comprehensive set of policies to serve the sector's and nation's best interests.Bracketed text and comments are not part of the draft report prepared by the contractor.The impact of various policy types on competitiveness is an important and timely issue none the less and is therefore the sole focus of this study. Method and ProceduresThe method and procedures undertaken to complete the study are outlined in Exhibit 1.1 which also illustrates the three streams which comprised the work plan.One stream involved the preparation of industry profiles to provide an overview of the Canadian and U.S. chicken and pork industries.The profiles were prepared on the basis of published secondary data which was analyzed and interpreted to determine the current structure and performance of the industry, changes in these over time, and likely future trends.These profiles provided background data for the assessment of policy impacts on the competitiveness of the Canadian and U.S. chicken and pork industries.[Profiles are not provided in this abridged version of the report.]A second stream of the work plan involved the identification of policy categories for the classification of policy instruments.Thirteen categories were defined and the policy instruments used in the Canadian and U.S. chicken and pork industries (i.e., the policy set) were classified into these categories.In addition, the relative significance of the policy categories was identified through the economic indicators: net benefits; producer subsidy equivalents (P.S.E's); and expenditures.The sources of policy instruments data were: the 1989 Net Benefits Results; the Hill and Knowlton Study of U.S. interventions; and published data used by the U.S.D.A. in estimating producer subsidy equivalents.Primary research into the specific instruments operating in Canada at the federal and provincial levels was required in order to obtain descriptions of the nature of these instruments so that they could be classified appropriately.In addition, primary research was undertaken to identify policy instruments for the chicken industry in the south eastern United States which was not covered by the Hill and Knowlton study.The core stream of the work plan involved the development and application of the analytical framework for assessing the relative impact on competitiveness of specific policy categories.The framework is composed of two elements: an economic theory perspective and a business systems perspective.The framework was applied to each of the thirteen policy categories.The economic theory perspective was analyzed by the core project team using a spatial equilibrium model with a homogeneous product and two trading regions.The business systems analysis included using a delphi team approach.The delphi team was composed of six agricultural economists (three in academic appointments in the United States and three in academic appointments in Canada).Each team member independently completed a detailed analysis of
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".