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Record W4411020229 · doi:10.61959/vbjg4051e

The Current State of Military Family Research

2016· report· en· W4411020229 on OpenAlexaboutno aff
Heidi Cramm, Deborah Norris, Linna Tam‐Seto, Maya Eichler, Kimberley Smith‐Evans

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurrent (fluid)State (computer science)Political scienceComputer scienceEngineeringElectrical engineeringProgramming language

Abstract

fetched live from OpenAlex

Since the 1990s, the nature, frequency, and intensity of military operations have shifted, and these shifts have, in turn, had an impact on the families of Canada’s military personnel. Operational tempo has increased and has been almost continuous, and the roles of Canadian Armed Forces (CAF) personnel1 have changed from “peacekeepers to peacemakers to warriors.” In 2013, the Office of the Ombudsman, National Defence and Canadian Forces released its seminal report on military family health and well-being, On the Homefront: Assessing the Well-being of Canada’s Military Families in the New Millennium. This report brought into view the contexts, meanings, and consequences associated with recent changes in CAF military operations for members, Veterans, and families.

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)0.026
metaresearch head score (Gemma)0.051
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.186
Threshold uncertainty score0.369

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.022
Science and technology studies0.0060.007
Scholarly communication0.0080.010
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0300.003

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 head0.259
GPT teacher head0.386
Teacher spread0.126 · 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
GenreReview

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
Published2016
Admission routes1
Has abstractyes

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