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Record W4293079260 · doi:10.1111/1748-8583.12459

The next mission: Inequality and service‐to‐civilian career transition outcomes among 50+ military leavers

2022· article· en· W4293079260 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

VenueHuman Resource Management Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsYork University
Fundersnot available
KeywordsInequalityOfficerWork (physics)General partnershipTransition (genetics)Focus groupService (business)Military serviceDemographic economicsStructural inequalitySurvey data collectionPsychologySociologyPolitical scienceBusinessEconomicsMarketingFinanceEngineering

Abstract

fetched live from OpenAlex

Abstract We examine the Service‐to‐Civilian career transition for Military leavers aged 50 and above (50+). The exit age of our sampled group means that it is more likely that they hold senior‐ranked positions across both Officer and Soldier career pathways. Despite both groups having access to similar transition opportunities and resources, we find that their work‐lives are underpinned with economic, social, and structural inequality. This inequality has substantive effects on their employment transition outcomes. Our focus group data suggest that Soldiers have unequal access to formal (e.g., Career Transition Partnership programmes) and informal (e.g., social networks) transition support resources compared to Officers. Employing a structural equation modelling approach to analyse 183 survey responses, we found that Soldiers are more likely to apply for, and subsequently take, civilian work that is below their skills level. In turn, Soldiers are significantly less satisfied with their civilian work than Officers.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.203
GPT teacher head0.380
Teacher spread0.177 · 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