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Record W2936264170 · doi:10.1080/23340460.2019.1601415

Why we need to think beyond NATO’s 2 per cent benchmark: suggestions for amending the research programme

2019· article· en· W2936264170 on OpenAlexfundno aff
Benjamin Zyla

Bibliographic record

VenueGlobal Affairs · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOperationalizationPositivismIndividualismPolitical sciencePoliticsMethodological individualismSociologyPositive economicsEpistemologyPublic relationsEconomicsLaw

Abstract

fetched live from OpenAlex

The research programme on NATO burden sharing is heavily influenced by quantitative political economists who are interested in testing theories with statistical inferences and regression analysis. It uses predominantly rationalist, deductive reasonings and is informed by methodological individualism. However, there are significant theoretical and methodological limitations with such an approach, above all in understanding burden sharing as a social practice rather than a static outcome. This contribution offers suggestions for a post-positivist turn of NATO’s burden sharing research programme, especially those that highlight the importance of intersubjective meanings and the role of social forces, norms, beliefs, and values in burden sharing decisions that are not derived from material interests and reflexively inform behaviour. Such a post-positivist turn, we charge, would help us to explain how collective burdens are constructed and perceived by the member states. We also offer suggestions on how to operationalize this turn in the research programme.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.076
GPT teacher head0.311
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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

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