Bibliographic record
Abstract
Note: Page numbers in italics refer to Figures; those in bold to Tables.adaptive resilience challenges, transportation security, 67 CI security requirement, 67 critical infrastructure protection (CIP), 68 definition, 66-7 infrastructural preparation, 68 national security agenda, 67 9/11 Commission policy recommendation, Homeland Security Act of 2002, 66 policy shifts, 68 regular and catastrophic risks, 66 research and practice structural and policy-level impacts, 68 total resilient ecosystem, 68 ADGP see Red Cross Annual Disaster Giving Program (ADGP) airport security policy, cost-effective antiterrorism security, 224 Aviation and Transportation Security Act (ATSA), 205 aviation security, 206-9 Canada, risk-based policy, 210-211, 225 Canadian Air Transport Security Authority (CATSA), 221 compensation levels, airport screeners, 224 Europe's steps toward risk assessment, 211, 225 Federal Aviation Administration (FAA), 222 International Civil Aviation Organization (ICAO), 209-10 mode specific, EU countries, 225-6, 227 "one-stop" security for intra-EU passengers, 226 paying, airport security, 224-7 policy decisions, 219 provision in Europe, 2011, 219-21, 220-221 risk-based approach, 213-19 Transportation Security Administration (TSA), 205, 222-3 TSA-screened Los Angeles International (LAX), 223 United Kingdom, 225
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.516 | 0.449 |
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".