A model of military to civilian transition: Bourdieu in action
Bibliographic record
Abstract
Building on recent work that used the ideas of sociologist Pierre Bourdieu to construct a theoretical framework for considering military to civilian transition (MCT), this article introduces a practical approach to develop the use of this theory into an adaptable framework to explore factors that affect MCT. We have devised a model of MCT called the Model of Transition in Veterans (MoTiVe) to explore why an enduring attachment to the military exists for Veterans and to develop an understanding of how “looking back” on life events experienced in the military may cause difficulty for some in transition. We use Bourdieusian theory to consider the adjustment of military personnel back into civilian life, taking into account the importance of individual variances in socio-economic trajectories, life stories, and subsequent discrepancies between the norms of the military and civilian environments. We suggest that MoTiVe is a useful tool to reflect on how life experiences, both within and outside of the Armed Forces, affect the transition process, which can also be adapted to consider periods of transition in all walks of life.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".