The Future of Work and Workers: Insights from US Labour Studies
Notice bibliographique
Résumé
The rollout of sophisticated digital tools -including advanced robotics, data analytics, machine learning and the Internet of Things -threatens to disrupt the distribution, role and nature of work in society.Raising the spectre of mass unemployment and social instability, researchers predict that technological progress will soon allow for the rapid automation of many tasks that are currently performed by humans.Already the pace of change appears to accelerate, with the spread of platform-based business models fuelling the growth of gig and crowd work.While reductions in labour supply due to demographic shifts and COVID-19 militate against mass displacement, the prospects for the offshoring of services enabled by information technology (IT) and even the most limited applications of artificial intelligence (AI) will challenge inherited divisions of labour across societies (Autor, 2015;Baldwin, 2016).Most workers, including those far up the skills ladder and those in high-status jobs, will experience some form of disruption to their work duties. 1 Concurrently, other trends such as climate change, financialisation and workplace fissuring threaten to accelerate the ongoing concentration of power across societies in the hands of the wealthy few, leaving workers with less bargaining power and greater uncertainty.Given these developments, it should be no surprise that anxiety about the future runs high.In the United States (US), this has translated into more diverse and more contentious political debates.On the one hand, new visions for pooling collective risk, including the introduction of universal minimum income schemes, have entered mainstream thinking.Yet, on the other hand, policy-makers often continue with long-running efforts to undermine the fiscal power of the state, on which such new policy schemes would rely.Moreover, as economic inequality has grown and younger cohorts' prospects have dimmed, elites have taken more assertive steps to ensure against downward social mobility.Private investments in academic credentials have been a central means for transferring privilege from one generation to the next, whether pursued within or outside of increasingly stratified public education systems, and with the frequent tendency of weakening public provision.At the same time, sections of the population experiencing status erosion have begun to express their grievances in more forceful -and at times violent -ways.Just when mastering the looming socio-economic transformation requires effective mechanisms for collective action, the public approval of societies' central political-economic institutions has fallen, from Congress and the Presidency to Corporate America.Sadly, this is more than justified, given that even mainstream scholarship has found "substantial support for theories of Economic-Elite Domination and … Biased Pluralism" (Gilens and Page, 2014: 564).At the same time, public support for unions is at an all-time high in the United States, according to recent
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,010 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 ».