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Record W2594760217

Was the Battle of Hastings really fought on Battle Hill? A GIS assessment

2016· article· en· W2594760217 on OpenAlexaff
Christopher Macdonald Hewitt

Bibliographic record

VenueHistorical geography · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsWestern University
Fundersnot available
KeywordsBattleVictoryHistoryMilitary historyArchaeologyDecisive victorySubject (documents)GeographyLawAncient historyPolitical scienceComputer scienceLibrary science
DOInot available

Abstract

fetched live from OpenAlex

Fought on a hillside in southern England in the fall of 1066, the Battle of Hastings has long been regarded as a seminal moment in British history, due to the profound changes the invading Norman conquerors brought to the British Isles. As such, the conflict has been the subject of significant historical analysis.  One aspect of the battle that has not drawn much attention in academic accounts, however, relates to its location. To this point, observers have generally accepted that the site of the conflict was “Battle Hill,” pointing as evidence to the nearby presence of Battle Abbey, erected by the Norman leader, William the Conqueror, to commemorate his victory. Yet to this point, no archaeological evidence has been found to support the fact that a battle once occurred here. Furthermore, there are some local historians who believe that other sites are plausible. This study retests the case for Battle Hill as the site of the Battle of Hastings through a re-examination of historical data using a GIS-based multicriteria decision analysis (MCDA) model. The results indicate that while Battle Hill is indeed a likely site for the conflict, another nearby location—Caulbec Hill is an equally if not more plausible contender. The study concludes by discussing the implications of this investigation for interdisciplinary research.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.615

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.023
GPT teacher head0.240
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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