MétaCan
Menu
Back to cohort
Record W3203041843 · doi:10.3138/jmvfh-2021-0015

Family planning in the U.S. military: The gendered experiences of servicewomen

2021· article· en· W3203041843 on OpenAlexvenueno aff
Stephanie K. Erwin

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
Fundersnot available
KeywordsMilitary serviceVariety (cybernetics)Promotion (chess)Face (sociological concept)Family planningMilitary personnelWork (physics)Affect (linguistics)PsychologyPublic relationsPolitical scienceSociologyPoliticsPopulationLawEngineeringDemographySocial science

Abstract

fetched live from OpenAlex

LAY SUMMARY Balancing family and work is always challenging for working women; however, military service presents especially nuanced and unique challenges to women serving in the U.S. military. Family planning, and in particular marriage and children, have distinct impacts on servicewomen’s professional careers. Their chosen professions often intersect and detract from their family planning choices. Within a larger study of gendered experiences, women from all four branches of the U.S. military, representing a variety of familial statuses and occupations, noted the complex and challenging intersections of family and work they encountered over the course of their military careers. As in other professions, military women bear disproportionate familial burdens compared with their male counterparts, and challenges pertaining to marriage and children regularly affect their professional careers. However, the military presents heightened professional demands on family planning, including marital status, marital partners’ professions, pregnancy, maternity, and parenthood. These additional challenges women in the military face regarding family planning often run counter to organizational efforts to encourage women’s participation, promotion, and retention in the military.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.345
Teacher spread0.273 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Military Veteran and Family HealthSame topicGender, Security, and ConflictFrench-language works237,207