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Record W1987429795 · doi:10.1017/s1743923x09990201

The Numbers Do(n't) Always Add Up: Dilemmas in Using Quantitative Research Methods in Feminist IR Scholarship

2009· article· en· W1987429795 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

VenuePolitics & Gender · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsScholarshipFace (sociological concept)Field (mathematics)Selection (genetic algorithm)SociologyEpistemologyFeminist theoryQuantitative analysis (chemistry)Political scienceSocial scienceFeminismGender studiesLawComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

How might one reconcile feminist international relations theorizing with quantitative methods, if they can be reconciled at all? This question has occupied a central place in the field of feminist international relations theory since its inception, and while it is not my intention to rehash it in its entirety here, my story sheds light on the difficulties that feminist IR scholars may face in choosing the quantitative route. My essay highlights these conundrums from different stages of the research process: formulation of the research question, selection of a research method, usage of quantitative methods, and the dissemination of the findings. I conclude with questions for further consideration.

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)0.009
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
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 integrity0.0000.001
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 head0.467
GPT teacher head0.580
Teacher spread0.112 · 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