Bibliographic record
Abstract
A framework for thinking about borders in post-globalization scaffolds insights of emerging theory exemplary of prevailing in-betweenness of the border world. Epistemological advances frame dissensus, power, belongingness, borderscapes and a-territoriality as ridges of knowledge above the collected and connected lore of border experience, yet this knowledge remains topographic and incomplete for understanding interstitial components of borders and bordering. At best, linked approaches employing multiple perspectives, engaging with borderlands, portraying borderscapes, and articulating agency and mobility have set the stage for recalibration of borders in globalization, and approximation of post-globalization borders. In a post-humanistic era, in which humans encountered limitations of nature and sparred with natural laws, states propped up borders and emphasized boundaries. The “border turn” is reactionary, and antithetical, a time when we need to be mindful of the branded border and anxious of our belongingness both within and beyond borders. New directions at the border are epitomized by the articles in this special issue: controlling “blue” (maritime) versus “green” (land) borders, border ethics, China’s energized borders, borders as magnets of activism and spectacle, seemingly distinct borders in dialogue, border approaches of effective temporality, and the re-engagement of borders and policy.
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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.016 | 0.052 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".