Incentives to support sustainable rural tourism in British Columbia
Bibliographic record
Abstract
A industria de turismo rural da British Columbia (BC) tem a aspiracao de contribuir para o aumento do bem-estar sociocultural, ambiental e economico da provincia. Os operadores compreendem a importância da sustentabilidade para o futuro dessa industria, contudo estao buscando incentivos para ajuda-los a melhorar suas praticas de negocios de modo a satisfazer essa aspiracao. Olhando para o desenvolvimento sustentavel com base em uma perspectiva estrategica, compreendendo os mecanismos de mudanca de comportamento e considerando as caracteristicas dos operadores como pioneiros na adocao de praticas sustentaveis, neste artigo, analisa-se o papel dos incentivos no sentido de encorajar a adocao de praticas de negocios sustentaveis, tendo como objetivo primordial a oferta de apoio teorico para auxiliar o turismo rural BC. O estudo consubstancia este artigo utilizou uma abordagem de investigacao de metodo misto, combinando triangulacao teorica — revisao de literatura, dialogo (grupo focal) e questionario e um painel de peritos — e triangulacao de investigadores. As principais conclusoes incluiram a importância das motivacoes intrinsecas para a mudanca, o reconhecimento de que a tomada de decisao empresarial e altamente dependente de uma forte rede de apoio e de uma abordagem pratica de aprendizagem, e que a remocao de barreiras pode ser usada para criar incentivos.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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".