Canadian beef producer survey, 2020
Bibliographic record
Abstract
These data were collected through an online panel-based survey. The survey was designed to better understand Canadian beef producers grazing practices (continuous, rotational or adaptive such as Holistic Management, Adaptive Multipaddock or regenerative grazing), their reported well-being, mindsets (management priorities, systems thinking, etc) and demographics. The panel was recruited and run by Kynetec which is a specialist agricultural polling firm, who recruited for the study from their proprietary Canadian Producer Database. The survey was stratified across the four largest beef-producing provinces, roughly proportionally to farm numbers: Alberta (n=85), Saskatchewan (n=45), Manitoba (n=35) and Ontario (n=35). No criteria were applied on the amount of beef production, and respondents could also have other commodities. However, all participants had to be over 18, either the sole or joint decision-maker on their property (not secondary), have beef as part of their gross farm sales in 2018, and they had to graze cattle rather than simply feed them. Participants were rewarded with $25. Confidence interval is estimated at 6.9%.The dataset contains two files: the study questionnaire (text) and survey responses (tabular).
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.020 |
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".