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Advising and Assisting in Iraq

2022· book-chapter· en· W4313173094 on OpenAlexaboutno aff
Stéfanie von Hlatky

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
Fundersnot available
KeywordsCredibilityPolitical scienceGovernment (linguistics)Public administrationKey (lock)Public relationsLawComputer securityComputer science

Abstract

fetched live from OpenAlex

Abstract The NATO Mission in Iraq started in 2018. The US-led Global Coalition against ISIS was looking to transfer some tasks to NATO as the country’s security forces turned to stabilization and post-conflict reconstruction, yet again. Many NATO allies have sent troops to Iraq before, and this is a key reference point through which gender considerations are assessed: women can be important community stakeholders but can also be the brides of ISIS. Making the wrong gender-based assumptions can lead to costly mistakes. The Canadian commander brings his female gender advisor to all planning meetings and tells his staff to listen: what she says goes, or NATO risks facing a credibility gap. One cannot tell the Iraqi government to make room for more women when one brings an all-male NATO team to the meeting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.025
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0250.006

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 head0.056
GPT teacher head0.302
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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