Bibliographic record
Abstract
Stearns County, Minnesota is the number-one dairy-producing county in the number-one dairy-producing region--the Midwest--in the country. The area has been home to German-Catholic immigrants from the Rheinland region of Germany since the mid- to late-1850s, when they traveled across Canada and the northeastern United States before finally settling on homesteads in Central Minnesota. 150 years later, the descendants of these settlers still live and farm the same area. Through it all, these farmers have kept a similar schedule: six days of fieldwork, Sunday for rest. And, nearly since the day they arrived in the area, that day of rest has featured one sacred leisure activity: baseball. The state of Minnesota boasts over 250 amateur baseball teams, the most of any state in the country. In the summer of 2012, I moved to Spring Hill, Minnesota, a town of 85, to play for the Spring Hill Chargers and work on the farm. My thesis lies at the intersection of farming and baseball, showing the reader both how little and how much has changed for these men who still farm their land of their forebears and still play America's game. A Summer in the Land of Milk tells my story of living and working in a rural area where the past hangs like a shadow and the future is frighteningly uncertain.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.017 |
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".