Grassland Conversion and Social Identity: Evidence from Western Canadian Cattle Producers
Bibliographic record
Abstract
CONTEXTConversion of perennial grasslands to cropland or urban areas results in significant losses of ecosystem services and wildlife habitat. There has been limited research on producer motivations and preferences surrounding land conversion decision-making.OBJECTIVEThis research aims to identify the underlying motivations of western Canadian cattle producers and understand how these differing motivations impact grassland conversion decisions.METHODSWe conducted a producer survey that included a social identity framework and discrete choice experiment to quantify factors driving grassland conversion. 339 producers completed our survey, and identity profiles were developed using confirmatory factor analysis. The results of the discrete choice experiment were analyzed using a binomial logit and latent class model. RESULTS AND CONCLUSIONSWe find that the decision to convert native grassland is impacted by the availability of crop insurance and respondent attributes like risk attitude, identity classification, and previous land conversion. Our latent class model revealed two categories of producers, “Conversion Cautious” and “Insurance Oriented”, who differ in their risk attitudes, levels of previous land conversion, and farm attributes. SIGNIFICANCEThe results of this research identify that the timing of insurance availability and producer characteristics play an important role in land-use decisions. Our work demonstrates that understanding producer preferences (including social identity) may aid in designing policies that balance agricultural productivity with land conservation.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".