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Record W2466177524 · doi:10.1057/9781137273956_7

The (D)evolution of a Norm: R2P, the Bosnia Generation and Humanitarian Intervention in Libya

2013· book-chapter· en· W2466177524 on OpenAlexaff
Eric Heinze, Brent J. Steele

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2013
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHumanitarian interventionNorm (philosophy)Responsibility to protectPolitical scienceIntervention (counseling)LawPsychologyHuman rights

Abstract

fetched live from OpenAlex

The March 2011 NATO intervention in Libya has been widely touted as evidence of a new international norm that has come about as a result of advocacy of the Responsibility to Protect (R2P) principle (Ban 2011; Gerber, 2011; Adams, 2011). Likewise, the Libya intervention has been characterized as an ‘unprecedented moment’ that is indicative of a new commitment and consensus by states that they have an obligation — indeed, a responsibility — to protect people who are being grossly abused by their own government (Williams 2011: 249). In contrast to the position that the Libya intervention is somehow groundbreaking or indicates the emergence of a new norm of humanitarian intervention that has been empowered by R2P advocacy, we argue that what enabled the Libya intervention is essentially an international normative environment that was brought about by precedents set during humanitarian interventions in the 1990s, while its proximate causes were the unique political and empirical circumstances that surrounded the Libya crisis. What supplemented this similarity in normative environments and confluence of political factors was a generation of policy analysts, advocates and practitioners who used the failures of the 1990s as a set of formative experiences that provided shortcut comparisons to springboard the Libya intervention.

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)0.000
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: none
Teacher disagreement score0.975
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.018
GPT teacher head0.250
Teacher spread0.232 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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