Imagining and Imaging Borders: Understanding Borderlands for Global Sustainability
Bibliographic record
Abstract
Abstract. Borders are increasingly complex human responses and social constructions in a world where globalizing forces confront basic human concerns for security and certainty. In an effort to provide a background to assess research directions for imaging borders, this paper explores what we know about borders, and what we do not know well about borders. Borders in globalization are the meeting points of globalizing forces of security, trade and migration flows with emerging technologies, self determination and regionalization around the world. We need to know more about how: self determination fuels secessions and new borders; borders result from complex rather than simple policy and governance issues; borders depend on the political clout of borderland communities; market and migration flows impact borders; and borders are always in motion. The paper shows how these organizing principles underlie the basic themes of border governance, flows, culture, history, security and sustainability. Finally, the paper offers two brief illustrations of border imaging to link this presentation to the following discussion of the workshop.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".