Farmers Willingness to Share Data: A Study of Saskatchewan Farmers
Bibliographic record
Abstract
Modern farm machinery captures geocoded data on all aspects of a farming operation. These detailed datasets are called big data. Although some of this data is useful to individual farmers, much of it has little value to the farmer that collects it. Capturing the true value of big data comes when it is aggregated over many farms, allowing researchers to find underlying trends. To analyze farmers willingness to share data we conduct a hypothetical choice experiment that asked farmers in Saskatchewan whether they would join a big data program. The choice tasks varied the type of organization that operated the big data program, and included financial and non-financial incentives. Heteroscedastic and random effects probit models are presented using data from a survey constructed for this study. The results are consistent across models and find that farmers are most willing to share their data with university researchers, followed by crop input suppliers or grower associations, and financial institutions or equipment manufacturers. Farmers are least willing to share their data with government. Farmers are more willing to share data in the presence of a financial incentive or non-financial incentive such as comparative benchmark statistics or prescription maps generated from the data submitted. Acknowledgement :
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".