Bibliographic record
Abstract
Active tourism associations, 57 Active transport, 137 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 in local enterprises, 116 Competition, 34 Complexity adaptive systems, 172-173 Conservation, 21-22 Consultative voices of Samarnon, 207-211 Consumer confidence, 80 Consumer expectations, 101 Coopetition, 19 Coronavirus Aid, Relief, and Economic Security (CARES), 115 COVID-19, 20-21, 46 impact on ASEAN tourism, 3-7 pandemic, 4, 6, 46, 88-89, 94, 96, 98, 110, 114, 172, 220 Philippine macroeconomic performance surrounding, 162-163 Cruise port, 166 Cruise ships, 169-170 Cruise tourism, 170 Culinary tour, 142 Cultural awareness, 188 Cultural heritage, connecting with, 186 Cultural immersion, opportunities for, 186 Cultural preservation, 23 Cultural significance, 200 Cultural tourism, 35 Cultural walking tours, 141-142 Cycle tourism, 139, 182, 188, 190-191 Cycling, 182 tours, 143 Department of Education (DepEd), 203-204 Department of Environment and Natural Resources (DENR), 40, 225 Department of Environment and Natural Resources-Protected Area Management Board (DENR PAMB), 199 Department of Health (DOH), 97 Department of Labor and Employment (DOLE), 72, 97 Department of Social Welfare and Development (DSWD), 206 Department of the Interior and Local Government (DILG), 97
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.792 | 0.590 |
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".