The universality of the machine: labour process theory and the absorption of the skills and knowledge of labour into capital

普遍性(动力系统) 劳动经济学 经济 物理 凝聚态物理
作者
James Steinhoff
出处
期刊:Work in the global economy [Bristol University Press]
卷期号:: 1-20
标识
DOI:10.1332/27324176y2024d000000025
摘要

This article contends that deskilling is best understood not as a distinct phenomenon, but as a component of a process Marx (1993: 694) called ‘absorption’. Absorption involves not only the extraction of capacities from labour but also their implementation in machines. The article reads Braverman’s (1998) analysis of Taylorism as a demonstration of how absorption entails a specific labour process of its own, which I call the absorption process. The nature of the absorption process is contingent on many social factors. This article focuses on a technical factor: the particular machines used to implement captured skills and knowledge, called here the infrastructure of absorption. Since technological capacities are ever-evolving under capital due to the continual revolutionizing of the means of production, infrastructures of absorption change over time and this necessitates new absorption processes. Braverman (1998: 132) pointed to a qualitative change in absorption with the digital computer, which he described in terms of a new ‘universality of the machine’. While Braverman rightly pointed out the computer as a novel infrastructure, he did not discern qualitative changes to the absorption process, seeing instead the extension of Taylorist processes of capture of knowledge and skill. I contend that a qualitative shift has become apparent since the rise of machine learning in around 2015. Machine learning enables a different absorption process of emergence which does not require the codification of captured knowledge. Much labour process theory (LPT) (and adjacent) research presumes that deskilling and automation operate in terms of a process of capture, however, I show that emergence presents qualitatively different means for both. I suggest that the infrastructure of machine learning presents the possibility of task-agnostic automation .

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
思源应助土拨鼠采纳,获得10
1秒前
fay关注了科研通微信公众号
2秒前
关尔匕禾页完成签到,获得积分10
2秒前
zbb发布了新的文献求助10
2秒前
徐徐徐徐完成签到,获得积分10
3秒前
明明发布了新的文献求助10
5秒前
6秒前
矢思然完成签到,获得积分10
7秒前
goujuan发布了新的文献求助30
8秒前
林lin完成签到,获得积分10
8秒前
银古发布了新的文献求助10
8秒前
9秒前
10秒前
杨葱头应助健壮的含灵采纳,获得10
11秒前
11秒前
15秒前
Sparks发布了新的文献求助10
16秒前
16秒前
19秒前
小唐完成签到,获得积分10
21秒前
1111发布了新的文献求助10
22秒前
隐形曼青应助green采纳,获得10
22秒前
23秒前
852应助liujing_242022采纳,获得10
24秒前
攀攀完成签到,获得积分10
27秒前
wangdh发布了新的文献求助10
27秒前
桐桐应助呱呱爱吃柚子采纳,获得10
27秒前
有有完成签到,获得积分10
28秒前
7Seven发布了新的文献求助30
28秒前
zbb发布了新的文献求助30
28秒前
充电宝应助1111采纳,获得30
28秒前
29秒前
小蘑菇应助明明采纳,获得10
29秒前
森山完成签到,获得积分10
29秒前
31秒前
上官若男应助树林采纳,获得10
32秒前
鱻鱼鱻完成签到,获得积分10
32秒前
文艺的傲白完成签到,获得积分10
33秒前
wanci应助狂野鸵鸟采纳,获得10
34秒前
抹茶麻薯完成签到,获得积分10
35秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Various Faces of Animal Metaphor in English and Polish 800
Signals, Systems, and Signal Processing 610
Photodetectors: From Ultraviolet to Infrared 500
On the Dragon Seas, a sailor's adventures in the far east 500
Yangtze Reminiscences. Some Notes And Recollections Of Service With The China Navigation Company Ltd., 1925-1939 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6348968
求助须知:如何正确求助?哪些是违规求助? 8164154
关于积分的说明 17176680
捐赠科研通 5405479
什么是DOI,文献DOI怎么找? 2862019
邀请新用户注册赠送积分活动 1839808
关于科研通互助平台的介绍 1689072