Bibliographic record
Abstract
In this thematic issue of RIAS, we address a number of issues about the border, drawing on perspectives from multiple disciplines in the social sciences and humanities, from anthropology to political science, economics, and literature, and including the works of scholars based in Canada, the US, and Germany. Their works engage issues of Indigeneity, Africandescendant populations, Franco-Canadians, Gender and Race, Colonialisms, and the more-than-human world. Topics include hunting, cross-border Indigenous relations, treaties, oil protests, immigration, domestic workers, historical memory, creative fiction, and the notions of borders as textures, zones, lines, connections, and cultural imaginaries. Our emphasis on combining social science and humanities approaches is essential to this work. Much previous work on the Canada-US border has tended to focus either on political/legal issues or on literary/media studies. Instead, we strive instead to bring multiple disciplinary perspectives into conversation and include artistic/visualwork. This volume thus contributes to a broader project than one that would center on nationalist interests—either the US or Canada’s—and rather brings to the study of bordering practices and border theory a continental approach, one that attends to the places and spaces that are and/or become the border.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".