Bibliographic record
Abstract
Part 1 Borders as Barriers Chapter 2 Hadrian's Wall Chapter 3 The Great Wall of China Chapter 4 The Berlin Wall Part 5 Borders, Migrants, and Refugees Chapter 6 Sahuayo, Mexico, and Its U.S. Colonies Chapter 7 Migration from Lesotho, Mozambique, and Zimbabwe to South Africa Part 8 Borders and Partitioned Groups Chapter 9 West African Boundary Making Chapter 10 The Jivaro People between Peru and Ecuador Chapter 11 Boundaries as Social Practice and Discourse: The Finnish-Russian Border Part 12 Borders, Perceptions, and Culture Chapter 13 The Attitudes of Youth toward the Other Side: The Finnish-Swedish and Finnish-Russian Borders Chapter 14 Meaning and Significance of the Canadian-American Border Chapter 15 Northern Ireland Chapter 16 The Fault Line between Israelis and Palestinians Part 17 Borders and the Environment Chapter 18 Environment, Development, and Security in Border Regions: Perspectives from Europe and North America Chapter 19 Disaster on the Danube Chapter 20 Border Regions and Transborder Conservation in Central America Part 21 Borders, Goods, and Services Chapter 22 The U.S.-Mexican Border as Locator of Innovation and Vice Chapter 23 Cross-Border Shopping: Canada and the United States Part 24 Maritime and Space Borders Chapter 25 Maritime Agreements and Oil Exploration in the Gulf of Thailand Chapter 26 New Borders: The Sea and Outer Space
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".