Bibliographic record
Abstract
Since the fall of the Berlin Wall, Europe has been increasingly presented as a postwar (if not post-history) continent. Many experts referred to Europeans as post-heroic consumers who believed wars were a bygone oddity. However, as a well-known saying warns: ‘Those who don't know their history are doomed to repeat it’. War and remembrance came out just months after Russia's unprovoked invasion of Ukraine. And yet, in Vladimir Putin's propaganda, Russia claims it was forced to attack its neighbour. Putin's line of argument is steeped in historical references. A key element of his narrative refers to the Second World War: he offers an ahistorical, deceitful and bizarre take on Soviet Russia's role in Hitler's total war. In it, Putin completely omits Moscow's role in the partition of eastern Europe. His war-making is predicated on the misremembrance of the past. In this Manichaean view, Russians are innocent victims and Ukrainians are Nazi perpetrators. I cannot think of a more forceful illustration of the negative outcomes that false war remembrance may have on contemporary world affairs. This book ought to be read as a warning against the potentially disastrous consequences of the politicization of war commemoration processes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".