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Record W4384030290 · doi:10.1177/08969205231185270

The Political Economy of Precarious Work in India: A Case of Languishing Social Policy?

2023· article· en· W4384030290 on OpenAlexaff
Pankil Goswami

Bibliographic record

VenueCritical Sociology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPrecarityNeoliberalism (international relations)Welfare statePolitical economyPoliticsWelfareCorporate governancePrecarious workUnemploymentContext (archaeology)Political scienceSocial policyWork (physics)SociologyEconomicsEconomic systemEconomic growthMarket economyLaw

Abstract

fetched live from OpenAlex

The paper critically dissects the contemporary policy landscape and its ability to counter precarious work for construction workers in the Indian context. By focusing on the governance challenges faced by welfare institutions and the pre-existing fault lines exposed by the pandemic, the paper argues that social policies are languishing and inefficient to respond to the challenges of growing precarity. The paper uses Breman’s conception of ‘Footloose labour’ to understand informality related to construction workers and Gilbert and Terrell’s social policy analytical framework to understand the institutional response. The two major arguments that make the social policy languish are the inability of the policy to alter neoliberal employment relationships and the operational challenges that institutions face in implementing welfare schemes for many footloose labourers. Moreover, the situation is further exacerbated by inherent contradictions of the state which is entangled between promoting economic growth through neoliberal policies while consecutively ensuring labour welfare. If the Institutional challenges persist along with the persuasion of neoliberal reforms, footloose labour is only going to be further marginalized and pushed to limits.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.483
Teacher spread0.407 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractyes

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