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Record W3022532222 · doi:10.7202/1068718ar

The Political Economy of Employment Regulation in Small Developing Countries

2020· article· en· W3022532222 on OpenAlexvenueno aff
James Arrowsmith, Jane Parker

Bibliographic record

VenueRelations industrielles · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsDeveloping countryPoliticsEquity (law)Civil societyGlobalizationAgency (philosophy)Context (archaeology)State (computer science)Political scienceDevelopment economicsEconomicsEconomic growthSociologyLaw

Abstract

fetched live from OpenAlex

It is generally accepted that employment regulation offers mechanisms to generate orderly economic growth as well as provide for the protection of workers. Both these efficiency and equity arguments particularly pertain to developing country contexts. The evolution and impact of employment law and industrial relations institutions in large developing countries is of growing interest to western scholars, but small developing countries have been ignored. This lack of research inhibits understanding of the political economy of employment regulation in developing country contexts. This article explores developments in labour regulation in three small developing countries in the South Pacific—Nauru, Tonga, and Papua New Guinea—that have been impacted by globalization and international labour regulation in different ways. The comparative research adopts a stakeholder analysis approach based on programs of qualitative interviews and documentary analysis. The paper identifies a number of structural and agency constraints on the development and effective implementation of employment regulatory systems that primarily reflect political factors. These include disorganized employment relations, under-developed civil society institutions, concentration of power networks, the under-resourcing and compartmentalization of state institutions and a broader context of political change and instability. These factors, which are related to country size as well as stage of development, subvert the introduction, implementation and review of employment regulation even where efficiency and equity arguments may be accepted by policymakers. The article concludes with a discussion of the implications and need for future research.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.012
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.286
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2020
Admission routes1
Has abstractyes

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