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Record W4403701386 · doi:10.1177/07311214241291550

“Business as Usual”? Human Rights NGOs’ Adaptation Strategies to Repressive Legislation

2024· article· en· W4403701386 on OpenAlex
Ina Filkobski, Eran Shor

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

VenueSociological Perspectives · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Contemporary Political Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsLegislationAdaptation (eye)Human rightsLaw and economicsPolitical sciencePolitical economyBusinessSociologyLawPsychology

Abstract

fetched live from OpenAlex

Over the last two decades, governments have increasingly been adopting legislative measures that limit civil society and human rights organizations. While several studies explored the response of nongovernmental organizations (NGOs) in nondemocratic regimes to such measures, the literature on the response of NGOs in liberal democracies remains scarce. We examine this by analyzing the case of Israel. We conducted in-depth interviews with 30 position holders in 13 human rights NGOs, as well as lengthy ethnographic participant observations in two of these organizations. Our findings show that organizational responses varied significantly, ranging from minor to very significant changes. Furthermore, the direction of these changes was not uniform. While some organizations chose to intensify and radicalize their message, others preferred to depoliticize and appease domestic audiences. We reflect on the possible drivers of such strategic organizational differences and discuss the more general effects of repressive legislation in liberal democracies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.749
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.062
GPT teacher head0.306
Teacher spread0.244 · 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