Bibliographic record
Abstract
Studies of sailor quality of life (QOL) reveal that shipboard life is one among several work and non-work factors that help explain retention plans and behavior (Schwerin, Kline, Olmsted, & Wilcove, 2006 Schwerin, M. J., Kline, T. L., Olmsted, M. G. and Wilcove, G. L. 2006. Validation of a work/non-work life model of quality of life and retention among Navy personnel. Paper presented at the Center for Naval Analysis Workforce Research Conference. May2006, Falls Church, VA. [Google Scholar]; Wilcove, Schwerin, & Wolosin, 2003 Wilcove, G., Schwerin, M. J. and Wolosin, D. 2003. An exploratory model of quality of life in the U.S. Navy. Military Psychology, 15(2): 133–152. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). The study of factors affecting satisfaction with shipboard life lacks serious exploration, with most of the research on shipboard habitability being conducted 25 years ago. In the present study, data from the 2002 Navy QOL Survey were analyzed to reveal the facets of shipboard habitability viewed as most and least satisfying, to create habitability subscales, and to apply those subscales in a multiple regression to better understand satisfaction with shipboard life. Results are related to the larger discipline of environmental psychology (Gifford, 2002 Gifford, R. 2002. Environmental psychology: Principles and practice , 3rd, British Columbia, , Canada: Optimal Books. [Google Scholar]). Implications of study findings on policy and research, study limitations, and recommendations for future research are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".