Bibliographic record
Abstract
Introduction The initiation of the Make in India programme is yet another statement of the desire of the government to increase employment in the country through the manufacturing route. Under this programme, the manufacturing sector is expected to contribute at least a quarter of India's gross domestic product (GDP) by 2020. However, recent events and discussions have brought to the fore the pessimism that not much employment possibilities emanate from the manufacturing sector due to its capital-intensive nature, which the sector had become for quite some time now. The worst fears on this issue have been accentuated with the increasing automation of manufacturing processes elsewhere in the world. Industrial automation is thought to have a deleterious effect on the creation of employment in different sectors of the economy, manufacturing included. This has given rise to an important debate, primarily in the context of developed countries where industrial automation has diffused manifold and that too over a much longer period of time. This debate, although originally in the popular press, has now been brought to the formal academic table by the publication of an influential and highly cited piece of research by Frey and Osborne (2013). Subsequently, one of the leading academic journals, namely the Journal of Economic Perspectives , organised a symposium on the theme of ‘automation and labour markets’ in its 2015 summer issue. Thereafter, there has been a series of studies by academic economists and multilateral institutions such as the Organisation for Economic Co-operation and Development (OECD 2016) as well. In the context, the purpose of the chapter is to understand the extent of the diffusion of automation technologies in Indian manufacturing and then analyse its effects on manufacturing employment. Concept of Automation A range of technologies are involved in industrial automation which manifests itself as both hardware and software. Employment implications of these various automation technologies vary considerably. The specific automation technology that has the most direct impact on employment is the use of multipurpose industrial robots. The International Federation of Robotics – IFR for short – defines an industrial robot as ‘an automatically controlled, reprogrammable, and multipurpose [machine]’ (IFR 2014).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.174 | 0.091 |
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".