Notice bibliographique
Résumé
Introduction Advances in technology and the outbreak of the COVID-19 virus in 2020 have significantly changed the way people work. The internet and applications (‘apps’) on smart devices (such as phones and tablets) now allow people to freelance directly with clients in what has been described as the ‘gig economy’. People now have access to food delivery apps such as Menulog and Deliveroo as well as ride-hailing apps such as Uber, and these are now used as part of the normal lives of many people. COVID-19 has also substantially disrupted the way many people work. In an effort to contain the outbreak of the virus, a number of governments imposed lockdowns which resulted with employees making a mass migration from working in offices to working from home. Even after lockdowns were lifted, this trend towards homeworking and remote working appears to remain popular and many employees now engage in hybrid working (which involves splitting the working week between the office and the home or another remote location). The rise of the gig economy and the post-pandemic shift to remote working, whilst giving convenience to many, also, for reasons that will be explained in this chapter, disadvantages certain workers and makes other groups of workers vulnerable to exploitation. This chapter will focus on examining both of these developments, to expose how they create risks of discrimination based on class and/or factors reflective of social background. Part I will examine platform work in the gig economy, to highlight how digital technology is used to fuel the exploitation and underpayment of certain socially and economically vulnerable migrant workers. This part of the chapter will then apply the recent decision of the Quebec Court of Appeal in Bécancour to illuminate how the Quebec Charter's prohibition on ‘social condition’ discrimination may have particular applications in underpayment and wage theft cases. It will also compare this with the legal framework in Australia to show that whilst the decision in Foot & Thai Massage highlights the potential of the FW Act's prohibition on adverse action based on ‘social origin’ to have similar applications, the law in Australia needs reform before it may be able to achieve this. Part II will examine the post-pandemic shift to remote working and homeworking, to show how it has potential to disadvantage workers at the convergence of class, social background, and other attributes.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».