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Record W1986058676 · doi:10.7202/018252ar

2007 Presidential Address of the CHA

2008· article· en· W1986058676 on OpenAlexafffundvenueabout
Margaret Conrad

Bibliographic record

VenueJournal of the Canadian Historical Association · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsUniversité de Montréal
FundersUniversity of TorontoFederation for the Humanities and Social SciencesYork UniversityTemple UniversityQueen's UniversityMcGill University
KeywordsThe InternetPublic historyPublic relationsPolitical scienceTheme (computing)Bridging (networking)Presidential addressMedia studiesSociologyPresidential systemPublic administrationLaw

Abstract

fetched live from OpenAlex

In keeping with the Congress theme of “Bridging Communities: Making Public Knowledge, Making Knowledge Public,” this paper reflects on issues relating to public history and the impact of the Internet — that most public of media — on the ways in which academic historians create and disseminate knowledge. It explores the rise of public history as a profession and field of study over the past three decades, the efforts of the Canadian Historical Association (CHA) since its founding in 1922 to reach a broader public, and the impact of the Internet on the work of professional historians. By raising questions about the role of academic historians in general and of the CHA in particular in bridging what on the surface seems to be the divergent interests of academic and public history, it contributes to a larger discussion that will almost certainly preoccupy CHA presidents for the foreseeable future: where academic history and the arts disciplines generally fit into the postmodern university and into the rapidly expanding world of knowledge fuelled by the Internet and its related technologies.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Editorial · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.733

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0870.019

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 head0.029
GPT teacher head0.195
Teacher spread0.166 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations7
Published2008
Admission routes4
Has abstractyes

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