How did East Sussex Really Appear in 1066? The Cartographic Evidence
Bibliographic record
Abstract
Military history has provided significant insight into the factors determining the outcome of armed conflict through time. At the same time, it often fails to adequately assess variables unrelated to historical accounts per se that may contribute to military outcomes. For example, in 1066, English and Norman forces engaged in a decisive battle near Hastings, U.K. Numerous historical accounts have chronicled this event, using a combination of eyewitness and participant testimony, as well as written records, and art forms. Few, however, have paid significant attention to the role of the local landscape in shaping events. In the case of Hastings, the battlefield itself provides an example of the way in which geography can contribute to our understanding of historical events. By applying environmental sources and a regressive cartographic analysis, this study demonstrates that there is, in fact, considerable evidence to suggest how the landscape appeared back to the time of the battle. This finding is significant, insofar as it opens the door to new research on the Battle of Hastings which may shed additional light on the events that occurred there and the factors that influenced the outcome of this crucial conflict in British history. It also reveals the importance of applying new methodological approaches to traditional disciplines such as history, to deepen and expand existing analysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".