Bibliographic record
Abstract
It can be difficult for researchers from outside the military to gain access to the field. However, there is a rich source on the military that is readily available for every researcher: military memoirs. This source does provide some methodological challenges with regard to truth and (self) censorship, nevertheless. This study questions how truth and (self) censorship issues influence the content of these military autobiographies. It shows that these issues are not only a concern for researchers, but also for military writers themselves. The study provides concrete quantitative data based on military Afghanistan memoirs published between 2001 and 2010 from five different countries: the UK, the US, Canada, Germany and the Netherlands. The majority of soldier-authors make some kind of truth claim in their books that they also substantiate. Military books published by traditional publishers do so significantly more often than self-published books. In books published in Anglo-Saxon countries soldier-authors make truth claims five times more often than do military authors from the Netherlands and Germany. At the same time, military authors also frequently admit to some form of self-censuring, so truth claims and self-censorship go hand in hand. From each of the countries studied, at least one author mentions being actively censored by the military, but most do not even mention it, making censorship a common, almost normal military feature. Making truth claims, mentioning being censored, or self-censoring do not influence the kind of plots these authors write either in a negative, or positive way.
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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 | 0.016 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".