Bibliographic record
Abstract
Active Seniors tool, 122, 140 Age of acceleration, 182 Age of digitization, 182 Ageing population, 15 Airbnb Experience, 133, 236 evaluation of creative tourism activities offer in South Europe by, 145-154 Amazon Mechanical Turk, 201 ANIMART, 81-82, 85 App creatour.pt, 207 Application Programming Interface (API), 192-193 Aqueduct Project, 188 ArcGIS Online, 192-193 Artificial Intelligence (AI), 185 Artisans, 122 Artists, 122 Athens Retro Festival, 82, 85 Augmented reality (AR), 180-181, 196, 206, 212 app by country and capital in Southern Europe, 211 in smart mobile devices, 195 COVID-19 pandemic, 1-2, 17, 41, 205-206, 224-225 COVIDSafe app, 206 Creative activities in urban and rural territories of Southern Europe, 46-50 Creative Austria, 38, 55 180 Creative Camp, 81, 83 Creative class, 133 'Creative consumers', 133-134 Creative Europe programme, 73 Creative experiences, 8-9 Creative industries, 121 Creative tourism, 1, 9, 36, 229 from complexity of tourism industry to requirements of new
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.783 | 0.824 |
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".