Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,656 | 0,654 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».