Bibliographic record
Abstract
The Trump Administration’s strategy to isolate North Korea includes a ban on Americans travelling there. The 2017 ban especially impacts nearly seventy Christian faith-based organizations (FBOs), which in the past two decades legally channelled hundreds of (mostly volunteer) workers and thousands of tourists to North Korea. Since the travel restriction, these faith-based workers and tourists have publicly joined the debate over the United States’ North Korea policy. They have acted as “norm entrepreneurs,” shaping an alternative, cognitive frame for engagement. This frame includes three claims: 1) foreign Christian workers and tourists are generally tolerated by the regime, so long as they obey existing laws and regulations; 2) these workers and tourists meaningfully contribute to socioeconomic development and religious freedom in North Korea; and, 3) the US government should not violate the rights of Americans to travel and practice their faith, as long as they engage in these activities in a safe and meaningful way. The faith-based frame for peaceful engagement resonates among American mainstream and evangelical Christian media; it links with America’s individualist and evangelical normative traditions of social change through grassroots, personal relationships. We narrate and assess this frame through interviews and other communications with over twenty workers and tourists (mostly US citizens) linked with FBOs.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".