When Your Boss is an Algorithm: Preserving Canadian Employment Standards in the Digital Economy
Bibliographic record
Abstract
The platform or ‘‘gig” economy is a rapidly growing economy in Canada. Between 2005 and 2016, the share of gig workers among all workers in Canada rose from 5.5% to 8.2%. These include independent contractors, select freelancers and platform workers. In 2018, 28% of Canadians aged 18 and older reported making money through online platforms. Research by Payments Canada in 2021 showed gig workers as representing more than one in 10 Canadian adults with more than one in three Canadian businesses employing gig workers. As the share of platform workers in the economy has grown, so has the discussion regarding the employment status of workers, particular in the platform economy.\nAmong other things, platform work has fundamentally altered the system for managing workers, with algorithms replacing human beings in the control and direction of service providers. Algorithmic management of the workforce is considered a defining feature of both web-based and ‘‘location-based” digital labour platforms.\nThis article briefly examines potential challenges created by the platform economy in determining the existence of an employer under employment standards legislation. This article also analyzes existing and prospective legal responses to these challenges, assessing the advantages and limitations of legal regulation of the platform economy in Canada.\n--\nL’e´conomie des plateformes ou « gig » est une e´conomie en croissance rapide au Canada. Entre 2005 et 2016, la part des travailleurs a` la demande parmi tous les travailleurs au Canada est passe´e de 5,5 % a` 8,2 %. Il s’agit notamment d’entrepreneurs inde´pendants, de pigistes se´lectionne´s et de travailleurs de plateformes. En 2018, 28 % des Canadiens aˆge´s de 18 ans et plus ont de´clare´ gagner de l’argent graˆce a` des plateformes en ligne. Une recherche mene´e par Paiements Canada en 2021 a montre´ que les travailleurs a` la demande repre´sentaient plus d’un adulte canadien sur 10, avec plus d’une entreprise canadienne sur trois employant des travailleurs a` la demande. A` mesure que la part des travailleurs de plateforme dans l’e´conomie a augmente´, la discussion concernant le statut d’emploi des travailleurs a augmente´, en particulier dans l’e´conomie de plateforme.\nEntre autres choses, le travail sur plateforme a fondamentalement modifie´ le syste`me de gestion des travailleurs, les algorithmes remplac¸ ant les eˆtres humains dans le controˆle et la direction des fournisseurs de services. La gestion algorithmique de la main-d’œuvre est conside´re´e comme une caracte´ristique de´terminante des plates-formes de travail nume´riques base´es sur le Web et « base´es sur la localisation ».\nCet article examine brie`vement les de´fis potentiels cre´e´s par l’e´conomie de plateforme pour de´terminer l’existence d’un employeur en vertu de la le´gislation sur les normes d’emploi. Cet article analyse e´galement les re´ponses juridiques existantes et prospectives a` ces de´fis, e´valuant les avantages et les limites de la re´glementation juridique de l’e´conomie des plateformes au Canada.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".