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Record W7001945970

The Loyalist regiments of the American Revolutionary War 1775-1783

2009· other· en· W7001945970 on OpenAlexaboutno aff

Bibliographic record

VenueOpenGrey (Institut de l'Information Scientifique et Technique) · 2009
Typeother
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsHistoriographyFrontierColonialismSpanish Civil WarFirst world warWorld War II
DOInot available

Abstract

fetched live from OpenAlex

This dissertation is about the Loyalist Regiments of the American Revolution, 1775-1783. These were the formal regiments formed by the British, consisting of Americans who stayed Loyal to the British crown during the American Revolutionary War. They fought in most of the main campaigns of this war and in 1783 left with the British Army for Canada, where many of them settled. The Loyalist regiments have been neglected by academic historians with only one major work on them as a group. The intention of this dissertation is to give them their proper place in the historiography of the American Revolutionary War and of eighteenth century military history. The dissertation is laid out in the following way. Chapter one, will be an overview of the history of Regiments, from their origins in Colonial days until 1783. It will assess how they were dealt with by the British and examine both organisation and combat. Chapter two is a thematic chapter looking principally at the organisation of the regiments as well as their motivation and composition. The next four chapters are case studies of three Loyalist regiments. Chapters three and four are a case study of the Queens Rangers. A database of all the soldiers who served in this regiment was created and is included with this dissertation. Chapter five is about the controversial regiment, the British Legion. Chapter 6 is a case study of the frontier regiment Butler‘s Rangers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.073
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.299
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2009
Admission routes1
Has abstractyes

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