Destination Marketing Organizations and Climate Change : The Need for Leadership and Education
Bibliographic record
Abstract
Destination Marketing Organizations (DMOs) operate at many levels ranging from the national to the municipal and have evolved over the years to respond to the geographical and political realities that are associated with tourism supply. Alongside providing information to potential visitors, DMOs work to make a destination attractive by showcasing its unique aspects and attractions. As the appeal of destinations, cost of doing business and the destination brand may be affected by the possible effects of climate change, this study aims to identify opportunities and threats to municipal and provincial/territorial DMOs and their members as well as identify measures they are undertaking to address the potential impacts. A study conducted of Canada’s provincial and municipal large DMOs was conducted in 2009. This research found that awareness of climate change in Canada’s tourism industry is increasing, but more efforts must be undertaken to mitigate climate change. To address climate change and tourism, this paper suggests doing three things: (a) DMOs need to demonstrate leadership about climate change education and mitigation to all their members; (b) government policy and action are needed to provide incentives for industry to address climate change; and (c) industry members require further education to take the steps necessary mitigate risk and to adapt. The internet has changed the DMOs’ roles and how they provide information to the consumer; as such, they have been presented with an opportunity to take on new roles as educational and marketing providers. This paper will outline in the current shifts among Canadian DMOs and will discuss the key issues that are applicable to DMOs worldwide.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".