Bibliographic record
Abstract
demand for low-carbon tourism strategies, 137-138 and low-carbon tourism, 138-140 postpandemic tourism trends, 136-137 recommendations, and policy implications, 143-144 role in promoting tourism experiences, 140-143 tourism, 139 Active travel, 139 African economies, 5 Agriculture, 60 Air traffic, 167 Airports, 166-167 terminal congestion, 167 Architecture, 164-165 Armed Forces of the Philippines (AFP), 199 Asian Development Bank (ADB), 93, 100 Asian financial crisis, 110 Association of Southeast Asian Nations (ASEAN), 4-5, 10 case studies in, 21-24 impact of COVID-19 on ASEAN tourism, 3-7 Economic Blueprint, 163-164 Economic Community, 163-164 region, 4-5, 22 Attraction, accessibility, accommodation, amenities, and activities (5A's of tourism), 31, 34, 36 elements of, 164-167 future-proofing tourism and, 172-173 survey of built environment complementation, 169-172 Business climate, 88-89 confidence, 80 and economic adjustments to "new normal", 94-97 entities, 94 models, 13 Canadian Trade and Investment Facility (CITF), 92-93 Capability building, 115 Carbon dioxide (CO 2 ), 138 Cash-for-work programs, 22 Causal loop diagram (CLD), 108, 111-112, 114 empirical evidence supporting, 119 on-process, 112-117 Challenges, 68, 228, 231 City cycling tours, 143 Coastal Underwater Resource Management Actions Project (CURMA Project), 54 Commendation, 120-127 Community engagement, sustainable travel and, 187 Community involvement, 117 Community-based tourism, 242-243 Community-centered tourism approach, 13-17 Community-driven tourism initiatives
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.610 | 0.788 |
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; both teacher heads agree on what is shown here.
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".