Bibliographic record
Abstract
The current renewed interest in the study of borders and borderlands is paralleled by a growing concern and debate on the possibility of a border model, or models, and of a border theory, or theories. Certainly, there is a new attention to theoretical consideration and discussion that could help sharpen our understanding of borders. In this essay, I argue that a model or general framework is helpful for understanding borders, and I suggest a theory of borders. The seeds of my arguments are grounded in a variety of discussions and in the works of border scholars from a variety of social science disciplines. My contention is that the literature on borders, boundaries, frontiers, and borderland regions suggests four equally important analytical lenses: (1) market forces and trade flows, (2) policy activities of multiple levels of governments on adjacent borders, (3) the particular political clout of borderland communities, and (4) the specific culture of borderland communities. A model of border studies is presented in the second part of this essay, and I argue that these lenses provide a way of developing a model that delineates a constellation of variables along four dimensions.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".