Bibliographic record
Abstract
demand, 156 gentrification, 155-156 Google Maps, 13 housing market effects, 151 Lisbon, 145 off-the-beaten-track experiences, 154-155 Paris, 151 Portugal, 137 tourism growth, 153 touristification, 152 transnational gentrification, 124 TripAdvisor, 13 Albergo Diffuso (AD), 7 hospitality model, 283-284 overtourism issue, 281-283 Tuscany, 284-292 Amsterdam, 1, 13, 43, 156, 268 anti-tourist sentiments, 103-104, 105-108 countermeasures, 108-110 displacement and avoidance, 110-111 Irridex model, 98-99 life cycle and social exchange, 98-99 new urban tourism, 101-102 privileged discontent, 84 residents' perceptions, urban contexts, 100-101 social representations theory, 88, 99-100 tourism development, 95-98 tourism policy, 88 Anti-tourist sentiments evolution, 103-104 explanatory factors, 105-108 Assault on Pleasure, 66 Authenticity, 163,
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.781 | 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".