Bibliographic record
Abstract
Extract from Introduction...Northern research. A big topic. An important one. Scholars, academics, practitioners, and community people are thinking about it a lot: how and why it’s done. We’re talking about how to repatriate it, about how to fund it, about how to ensure that inquiries are relevant and methods valid, that people are involved in research in good ways, and that the research benefits widely.This is the place where “Tag the Scientists” comes from. Deep in the boreal forest, CritterLab, with its moose PI, fox and porcupine grad students, and bunny undergrads, undertakes an observational study of southern scientists who conduct research in and about the North, to uncover the complex lives of their subjects through remote sensing. It’s a riff on ACCESS, an idea facetiously floated by Aron Senkpiel and Norm Easton in the Northern Review’s first issue, recounting a time they’d been talking about “the problem of the South.” They had joked around with the idea of a northern Association of Canadian Colleges Engaged in Southern Studies. It would hold annual Southern Studies conferences in the North, and establish scholarships for students to come north to study southern Canada. The Association would set up field stations in the Near, Middle, and Far South to enable researchers to spend a month or two down south in the winter. “That reminded us,” they breathlessly conclude, “that we would have to give some thought to developing a code of ethics to which members engaged in southern research would have to subscribe.”(1) The tables would be comprehensively turned!
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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.869 | 0.869 |
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".