Learning to ride the waves: Whale-watching tour operators in British Columbia and their perspectives on COVID-19 impacts
Bibliographic record
Abstract
The COVID-19 era and related restrictions have impacted British Columbia (BC) whalewatching operators and their perspectives on whale-watching and tourism-government relations in BC. Despite the popularity and importance of whale-watching in BC, and its relative freedom from restrictions given its outdoor operations, tour operators had to negotiate dynamic constraints during the COVID-19 era. Federal and provincial government responses to the pandemic directly impacted whale-watching, for example, by changing the capacity or number of tourists allowed per vessel. Restrictions also impacted additional goods and services offered by some tour operators, such as refreshments. Adapting to such changes required a certain agility on the part of operators. Many operators also accessed key government supports such as loans and wage subsidies. This paper is based on a mixed methods research project centered on qualitative interview-based research and analysis. It was also informed by limited participant observation on whale-watching tours. Here, we present data gleaned from virtual interviews with 10 whale-watching tour operators. These operators represent approximately 1/4 of 39 active existing operators on and around Vancouver Island, British Columbia, Canada. We offer participant responses and greater response patterns with respect to: 1) how COVID-19 impacted whale-watching operations in BC, 2) what, if any, pivots or changes operators made in response, 3) which supports they accessed and their evaluations of them, and 4) their perspectives on the future of BC whalewatching. We begin with an introduction to: whale-watching in BC; COVID-19’s impacts on tourism as well as government responses to these; key concepts such as the Tourist Area Life Cycle, and ideas about how tourism weathers crises. We then present results highlighting key barriers, opportunities, and adaptations experienced by the tour operators, emphasizing their own words. We end by considering longer term implications for whale-watching in BC. This paper was written for both academic and applied audiences.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".