Bibliographic record
Abstract
Even though the concept of nationhood is a fairly recent and Western idea, it has left its mark not only on history itself, but on the way we are able to think, understand, and teach history. I have chosen a specific topic in German history, the stab-in-the-back legend of the First World War, which traditionally has always been conceptualized as a mere German discourse. Through an analysis of French newspapers, however, I was able to show that the myth of a never militarily defeated German army had meaning and consequences not only for German domestic politics, but transgressed national borders. It was a provocation well noted abroad, a journalistic tool to criticize French foreign politics, and an increasingly important influence of the war memorial in interwar France. This study revealed transnational aspects of history that are commonly overlooked when working within national frameworks. While history textbooks one hundred years later emphasize the great impact of the stab-in-the-back legend for German mentalities, we limit our views to national boundaries, and fail to acknowledge the impact of a globalized media industry. Not only was the German legend familiar to large parts of the French public, it also served as a historical site where the French national identity was formed against the backdrop the German “other.” The legend’s discourse thus perpetuated war images that legitimized French war efforts and losses. Hence, a transnational perspective of history does not negate the concept of nation, but transcends it.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".