Bibliographic record
Abstract
I am from the prairie ecosystem.It is the original homeland of the nêhiyawak (Cree), Anihšinapêk (Saulteaux), Dakota, Lakota, and Nakoda Nations, and the homeland of the Métis peoples.Before that, it was the land of the dinosaurs-a landscape that eventually gave rise to grassland bison and birds.I grew up in Regina and went to the University of Calgary for an undergraduate degree in political science.During summer breaks from university, I worked for TransGas, the government-owned and -operated natural gas company in Saskatchewan.For three consecutive summers, my job, for the most part, was to drive around the southern part of the province fixing pipeline post markers.I explored the prairie and toured small towns.I became an expert of sorts in rural Saskatchewan Chinese food and dive bars.I was not an environmentalist and knew virtually nothing about prairie plants, birds, and animals.I had to leave the province before I became interested in all of that.I completed my PhD in environmental policy at Purdue University in Indiana.Trading in wheat fields for corn fields, I felt more at home in Indiana than I had expected.My supervisor was the
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.283 | 0.113 |
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".