MétaCan
Menu
Back to cohort
Record W2319730318 · doi:10.1177/0894439316634077

Transnational Advocacy Networks

2016· article· en· W2319730318 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Science Computer Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsConcordia University
Fundersnot available
KeywordsState (computer science)Political scienceAmbivalencePublic relationsBusinessComputer scienceSocial psychologyPsychology

Abstract

fetched live from OpenAlex

We examine the costs and benefits of nongovernmental organization (NGO) networking using a complex systems approach and agent-based modeling to simulate the effects of NGOs’ efforts to seek influence in policy making at home and abroad. We elaborate on the boomerang model developed by Keck and Sikkink and uncover macro-level effects of multiple NGOs networking for policy influence in multiple states around multiple positions on the same issue simultaneously. The results of our model and simulations lead us to argue that the boomerang effect has interesting unexplored implications for NGO behavior and state policy worthy of further empirical testing. We find that networking is necessary for NGOs to change state policy but leads to a higher likelihood of organizational collapse for NGOs. Although networking leads to policy change, as is well demonstrated within existing literature, our model suggests that efficacy comes at a cost to NGOs, which should make analysts and academics more ambivalent about the advisability of NGO networking.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.349
Teacher spread0.318 · 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