Bibliographic record
Abstract
abstract To date, 1.7 million US military service personnel have been deployed to Iraq and Afghanistan. Of those, one in five are suffering from diagnosable combat‐stress related psychological injuries including Posttraumatic Stress Disorder (PTSD). All indications are that the mental health toll of the current conflicts on US troops and the medical systems that care for them will only increase. Against this backdrop, research suggesting that the common class of drugs known as beta‐blockers might prevent the onset of PTSD is drawing much interest. I urge caution against accepting too quickly the use of beta‐blockers for dealing with the psychological injuries that combat experiences can wreak. Beta‐blockers are thought to work by disrupting the formation of emotionally disturbing memories that typically occur in the wake of traumatic events and that in some people manifest as PTSD. Focusing on a single dimension of soldiers' experience in combat, namely, their perpetration of other‐directed violence, I argue that some of the emotional memories blunted by beta‐blockers play important roles in the recovery of moral aspects of soldiers' selves damaged by experiences of combat violence — specifically, in the achievement of a state of grace— and, therefore, that the use of beta‐blockers may come with distinct moral costs.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".