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Record W7056465879

Explaining Early Adoption : National Action Plans on Business and Human Rights

2021· article· en· W7056465879 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsNormativeHuman rightsPublic policyLegitimacyGovernment (linguistics)ReputationAction (physics)
DOInot available

Abstract

fetched live from OpenAlex

Diffusion of innovations theory concerns the process by which innovations are communicated through the members of a social system. Previous research has shed significant light on how public policies diffuse across governments over time, but there is little understanding of why they diffuse. The answer may lie in the motivations of early adopters. When governments are the first to adopt policy innovations, they lack knowledge about the political, economic, and other costs of adopting the policy. Given the potential risks, it is not obvious why a government would want to be the first to adopt a policy innovation. This thesis investigates the question of what explains early adoption of policy innovations. It contributes to the international relations literature on policy diffusion by proposing a theoretical framework for studying early adoption that consists of four motivations: 1) Normative – the government adopts a policy because of a normative position on a particular objective; 2) Reputation – the government seeks to improve its image or garner legitimacy in the international community; 3) Competition – the government seeks to gain a competitive edge on other states in “races to the top”; and 4) Domestic lock-in – the government adopts a policy to “tie the hands” of future national governments. The thesis has an empirical focus on public policies for regulating corporations on human rights issues: National Action Plans on business and human rights (NAPs). These plans are national governments’ strategies for implementing the UN Guiding Principles on Business and Human Rights (UNGPs), a set of global policy norms that provide guidance for states and corporations on addressing the human rights impacts of business. As this field is largely neglected by political scientists, the thesis makes an additional empirical contribution to the burgeoning interdisciplinary literature on business and human rights. The theoretical framework is applied in a two-step, mixed-methods research design that includes a global mapping of NAPs and hypothesis testing. The thesis then presents three sets of comparative case studies: Colombia/Ecuador, United States/Canada, and France/Sweden. In the first four case studies, the theoretical framework is used to compare early adopters and laggards. In the final case study chapter, two early adopters are compared to determine whether there is potential to explain variation within the adopter category. The findings lead to several conclusions. First, normative commitment can provide a strong motivation for early adoption, and domestic actors are particularly important for shaping a government’s normative preferences. Second, governments with concerns about their international reputations are more likely to be early adopters, especially if reputation gains are linked to a reward. Third, governments act strategically to trigger races to the top, especially when they are more economically powerful. They thus adopt particular styles of regulation early to influence the style of regulation adopted elsewhere. Fourth, the desire to lock a policy in place domestically is an especially powerful motivation for early adoption, although it is not essential. Governments may seek to lock policies in place both in advance of imminent political loss and in the wake of domestic political strife. Finally, interactions between these motivations may give them more explanatory power and may explain the relative stringency of the policy adopted. Reputational concerns and the desire to lock policies in place are especially mutually reinforcing.

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)0.011
metaresearch head score (Gemma)0.023
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.017
Scholarly communication0.0050.011
Open science0.0010.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.000

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 head0.035
GPT teacher head0.302
Teacher spread0.268 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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