Bibliographic record
Abstract
For several years, I have lived within the shadow of one of Sweden's major ski resorts, a community boasting just over 3,000 permanent inhabitants.This destination attracts both domestic and international visitors, most of whom arrive during the winter months.To be sure, the situation we encounter in this popular Swedish mountain resort plays out in numerous small communities worldwide, whose geographical assets have transformed them into popular destinations.Such places, including seaside and lakeside resorts, mountain settlements and gateway communities to national parks regularly suffer from a host of seasonality-related problems.Given their small populations and narrow employment base, these resort communities rely heavily on temporary workforces made up of individuals from other places.Often, these workers are international migrants.In the case of the Swedish resort, some workers are those with few options for employment such as recent migrants to the country who are granted temporary work placements.These individuals usually work behind the scenes, performing low skill tasks such as room cleaning or dishwashing.The type of work they perform means that they rarely, if ever, come into direct contact with the visitors.Meanwhile, the same destination also attracts its fair share of lifestyle migrants, namely Swedes and others who are drawn to the destination, primarily because they wish to participate in their favorite activity such as winter sports.To them, working in the destination is a means to an end; it enables them to participate in a pursuit they enjoy, such as mountain biking or cross-country skiing.Among these lifestyle workers, we encounter Canadian and Italian ski instructors who work there for part of the year.When the winter season is over, some of them take up temporary assignments at winter sport destinations in the southern hemisphere (e.g., New Zealand).Topics such as the mobility of tourism work and workers have long grabbed my own attention as a researcher who, in recent years, has been focusing increasingly on issues revolving around the social equity dimension of sustainable development.One thing I have noticed in my own investigations is that, despite the emergence of the subdiscipline of labor geographies in Human Geography, to this day, few scholars have chosen to examine the spatial dimensions of tourism work and workers.Indeed, the by-now-sizeable volume of literature on tourism work and workers focuses mostly on themes such as the quality of jobs, narratives concerning the poor skills and wages related to this employment sector, labor turnover, calculations of employment multiplier effects to name but a few.I was, therefore, pleasantly surprised to see this impressive contribution by William Terry, a scholar who in recent years has emerged as one of the leading voices in the field of the labor geographies of tourism workers.In this well-woven volume, Terry once more highlights his skills as a researcher and writer, traits he displayed in his earlier pathbreaking examinations of the geographies of Filipino cruise ship workers.Specifically, he offers us a theoretically and empirically rich study of
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.656 | 0.654 |
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".