Promotion of Social Inclusion through New Steps in Tourism
Bibliographic record
Abstract
Social inclusion is one of the interesting topics of our times, taking attention in both \npolitical realm and scientific inquiry. For instance, social inclusion is considered among the main \ngoals of rural development programmes (Shortall, 2008), within the mega-events such as Vancouver \nOlympics in 2010 (Vanwynsberghe et. al., 2013), related to maintaining mental health (Repper & \nPerkins, 2003) and even subject to transportation policies (Pagliara & Biggiero, 2017). By definition, \nsocial inclusion is about making sure all individuals are able to participate as valued, respected and \ncontributing members of the society on the basis of five principles: valued recognition, human \ndevelopment, involvement and engagement, proximity and material well-being (Donnelly & Coakley, \n2002). Social inclusion plays a key role in creating a stable social order premised on social action; \nhowever it is dependent on the openness of political structures in a country (Shortall, 2008).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".