An investigation into the willingness-to-pay for branded fresh beef products in Canada
Bibliographic record
Abstract
Thele is currently very little fresh brand name beefsold in grocery stores across Canada.This is in contrast to the United States, where brand name beef is available in nearly every grocery store.Thetwo countries' branded fresh beef selections for consumers have evolved differently for one or more reasons; one potential reason maybe because Canadian consumers are not willing-to-pay a premium for value added beef with a bland name.To date, it has never been formally determined whether Canadian consumers are willing-to-pay fol brand name beef.A branded beefproduct would offer consumers a gualanteed consistent product and other impoftant attributes ofa typical brand.The objective ofthis thesis is to determine if Canadian consumers are willing-topay for bland name beefproducts.To address this objective, Becker-DeGroot- Marschack experimental auctions were conducted in Canadian grocery stores.In addition an open-ended survey involving a cheap talk script was mailed to fuilher measure willingness-to-pay across Canada.Several hypothetical brands were created to represent the various types of fresh beefbrands currently available in the United States.Data collected from the experimental auction and cheap talk sulvey were analyzed using limited dependent variable models such as the tobit and doubleìurdle models.These models were used to determine whether Canadian consumers were willing-to-pay for brand name beef and to determine which types ofconsumers were willing{o-pay for the valious types ofbrands.First and foremost I would like to thank Luke who gave me invaluable encouragement tlloughout my entire academic career.The completion ofmy two degrees would have been so much more difficult without you by my side.Luke, your unwavering support during my education has allowed me to have the time of my life.I owe deep gratitude to my family for supporting me and putting up with me tluoughout my entire education.All ofyou have made me feel as though I could reach whatever goals I set for myself.Thank you to my supewisor, Dr. Jared Carlberg, for always being around and having time for me no matter how busy you
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".