Bibliographic record
Abstract
BOOK REVIEW Shelley MacDermid Wadsworth and David S. Riggs (Eds.). Military Deployment and its Consequences for Families. New York: Springer Publications (2014). 336 pages, ISBN: 978-1-4614-8711-1.Military Deployment and its Consequences for Families brings together the contributions of scholars, clinicians, and policymakers who attended the 2011 International Research Symposium on Military Families. It is intended for researchers and practitioners working the field of mental health and is divided into four sections: marital and functioning; parenting and child outcomes; single service members, and sequelae of wounds and injuries. Within each, there is a mixture of research that expands on existing findings - such as Knobloch and Theiss's study (Chapter 3) of post-deployment reintegration or Fraciskovic, Stevanovic and Klaric's analysis (Chapter 15) of families and PSTD - as well as research that ventures into new territory, such as Oswald and Sternberg's work (Chapter 8) on Lesbian, Gay and Bisexual (LGB) families in the American military.The LGB chapter, the separate section on single members, and the emphasis on parents' as well as spouses' roles (e.g., in Griffin et al.,'s study of caregiving in Chapter 14) helps to counter the bias present in much of research toward viewing as including only those relationships in which the member is a spouse and parent - what Dorothy Smith (1993) has labelled the Standard North American Family (SNAF) made up of a male primary breadwinner, a female mainly responsible for work within the home, and dependent children. Military policies regarding who is eligible to receive benefits and supports as a member of a military family and lifestyles that isolate immediate families from broader social supports (Daigle, 2013) tend to reproduce SNAF, such that researchers have to work hard to move beyond this bias and examine those lives that are less visible.The studies cover a broad range of qualitative and quantitative methodologies, including diary methods, interviews, survey research, document analysis, and the use of administrative data to examine health outcomes on children with deployed parents (Larson et al., Chapter 6), an innovative technique rarely seen outside of population health surveillance. This provides for a well-rounded body of knowledge that recognizes that different methods can afford different views of the subject. However, the relatively narrow focus on psychological perspectives may be disappointing for those who are seeking a broader range of contributions.Although it generally presents a solid collection of recent scholarship on an emerging area of interest to studies, the book suffers from a few limitations that are common to collections of conference proceedings. Editors of such an anthology face the dilemma of whether to stay true to the conference and report exactly what was presented, or to move beyond this, recognizing that a book is a different medium that can be used to accomplish different things. In this case, the book would have benefited from some judicious editing on a few points. First, while comprehensive literature reviews are essential for standalone articles, in a book these reviews can get repetitive and some trimming in articles on similar subjects would have allowed for more in-depth explorations of the studies' original contributions. Secondly, two chapters (Hoobler's work in Chapter 12 on women and work and Degeneffe and Tucker's survey of brain injury services in Chapter 16) dealt mostly with research on the civilian sphere and the ties to the population needed more attention. …
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".