Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 0.002 |
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 it