Use and Adoption of Marketing Methods by Western Canadian Producers
Bibliographic record
Abstract
The use of forward marketing techniques is viewed in this thesis as a technological adoption by the producer and has been an area of interest for research in the United States since the 1980s. Unfortunately this research has not occurred in Canada. Reasons for this lack of research may include that one institution was legislated the task of marketing the majority of Canadian export wheat. As of August 12th, 2012 Canadian policy has changed to allow all Canadian wheat to be marketed by producers. This thesis attempts to add some geographic diversity to the literature by asking Canadian producers for a history of their marketing choices. Comparisons are made between the two most prevalent crops of wheat and canola. This thesis also takes advantage of the major change in policy to attempt to identify similarities between producers who adopt specific marketing techniques. Producers were directly surveyed on their use of marketing techniques from the year 2010 through 2013. Data from a total of 295 responses are presented to give an overview of what technologies are being used to market wheat and canola by producers on the Canadian prairie. Producers were also asked questions about their personality and farm enterprise, and to identify if they had increased their use of a marketing technique after the policy change of 2012. The responses were analyzed through probit regression. This thesis finds that forward contracting is the most prevalent marketing technique used by Canadian producers for wheat and canola. Use of futures markets and options markets are a distant second and third respectively. Rates of use by Canadian producers are found to be similar to rates reported in US research. Regression analysis of producers who increased their use of similar marketing techniques suggests that they share similar personal attributes. Forward contract use is found to increase with previous experience in use. Futures market use is found to increase among those inclined to believe that they control the world around them. Options market use is found to increase among those most comfortable with risk. Several farm demographic variables are also found to be shared among producers, and these findings are found to support similar findings in previous research from the US.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".