Bibliographic record
Abstract
The premises of this exploration in border theory are that borders are always in motion, that our theories about borders need to reflect this axiom beyond acknowledging borders as process and changing quality, and that these theories need to align with the “motion turn” in the social sciences. After characterizing and visualizing borders in motion, the paper evaluates the potential building blocks for a theory of borders in motion. These include concepts of border construction and reconstruction, exercise of power, equilibrium seeking, vacillating borders, spaces of flows, and uncertainty in transition space, among others. Analogues from basic and environmental science are postulated to explain how motion operates to generate bordering and create borders and borderlands, as well as account for movements surrounding borders and their alteration and reconciliation. Three component realms of a conceptual framework are offered: generation and realization of borders through dichotomization and dialectic, border dynamic motions and signatures, and alteration and reconciliation of the border in response to breaking points. The evolving framework is articulated with reference to a case study from the Pacific Northwest border region between Canada and the United States.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".