MétaCan
Menu
Back to cohort
Record W4220736460 · doi:10.1080/17502977.2022.2031520

You Cannot Improve What You Do Not Measure – The Gendered Dimensions of UN PKO Data

2022· article· en· W4220736460 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Intervention and Statebuilding · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsnot available
FundersGlobal Affairs Canada
KeywordsPeacekeepingSituation awarenessGender mainstreamingSituational ethicsMainstreamingGeospatial analysisMeasure (data warehouse)Political scienceSociologyPublic relationsPublic administrationComputer scienceEngineeringGender equalityGeographyLawGender studies

Abstract

fetched live from OpenAlex

UN peacekeeping operations have introduced new data-based systems such as the Situational Awareness Geospatial Enterprise (SAGE) and the Comprehensive Planning and Performance Assessment System (CPAS). Simultaneously, UN leadership has repeatedly made the case that more women in peacekeeping will make peacekeeping more effective. We argue and show that these initiatives while occurring concurrently have been separate and that there is a lack of gender mainstreaming in the data-based approaches. We contend that this has negative consequences: It produces incomplete data regarding threats and needs of local beneficiaries and peacekeepers, it impedes performance assessment, and it leaves inefficiencies unaddressed.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.064
GPT teacher head0.352
Teacher spread0.289 · 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