Demography and Environment in Grassland Settlement: Using Linked Longitudinal and Cross-Sectional Data to Explore Household and Agricultural Systems
Bibliographic record
Abstract
The Demography and Environment in Grassland Settlement project (DEGS) is a study of the relationship between population and environment in Kansas during its settlement and conversion from grassland to grain cultivation and rangeland. The research team involved in this project had as its goal to bring together data about farms and farm families in order to understand the core transformations in land use and family dynamics that took place during the process of settling and developing an agricultural landscape. For reasons we will explain later, the state of Kansas – located near the centre of the U.S. in a grassland ecosystem – is ideally suited for this study by virtue of its location, history and the documents that exist about it. In order to capture the environmental variability of Kansas, we are assembling a linked database of farm and family census records for twenty-five townships scattered across the state. This paper is about the process of choosing that sample, about the data we have accumulated and about the process we are undertaking to link records about families and farms through time and to attempt to find their locations in space.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".