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Record W2103218317 · doi:10.7202/1008977ar

Writing History in Macaulay’s Shadow: J.R. Seeley, E.A. Freeman, and the Audience for Scientific History in Late Victorian Britain

2012· article· en· W2103218317 on OpenAlexvenueno aff
Ian Hesketh

Bibliographic record

VenueJournal of the Canadian Historical Association · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophy, History, and Historiography
Canadian institutionsnot available
Fundersnot available
KeywordsAsidePoliticsHistoryShadow (psychology)NarrativePolitical historyHistoriographyLiteratureIntellectual historyNormativeSociologyClassicsLawArtPolitical scienceEconomic historyPsychologyPsychoanalysis

Abstract

fetched live from OpenAlex

So similar were the historical mindsets of J. R. Seeley and E. A. Freeman that there is still some confusion about which one coined the famous dictum that “history is past politics, politics present history”. Not only did they agree about history’s proper subject matter, they were also leading members of a community of historians in late Victorian Britain who sought to promote history’s scientific status against men of letters who were more interested in history’s literary potential. Seeley and Freeman seemed particularly to relish in exposing such historical imposters while promoting their own vision of history as a scientific and autonomous discipline. These similarities aside, the two diverged considerably when reflecting on the posthumous legacy of one such man of letters who likely did more than any other to popularize English history in the nineteenth century. Whereas Seeley believed that Lord Macaulay harmed history’s development by corrupting the general reading public’s historical sensibilities, Freeman argued that historians owed Macaulay a great debt of gratitude for not only basing his narratives on factual accuracy but also for doing so while reaching an extremely large audience. In debating about Macaulay, it is clear that Seeley and Freeman had different conceptions about the normative audience for a professional history.

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)0.003
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.191
Teacher spread0.163 · 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
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

Citations4
Published2012
Admission routes1
Has abstractyes

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