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Record W2740630762 · doi:10.7202/1040560ar

Canadian History Blogging: Reflections at the Intersection of Digital Storytelling, Academic Research, and Public Outreach

2017· article· en· W2740630762 on OpenAlexvenueaboutno aff
Tina Adcock, Keith A. Grant, Stacy Nation-Knapper, Beth M Robertson, Corey Slumkoski

Bibliographic record

VenueJournal of the Canadian Historical Association · 2017
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachStorytellingPublicationPublic historyMedia studiesNarrativeDigital storytellingSociologyPublic relationsPublishingDigital scholarshipValue (mathematics)Inclusion (mineral)Political scienceLibrary scienceSocial sciencePedagogyArtLiteratureLaw

Abstract

fetched live from OpenAlex

This article surveys the impacts of blogging on Canadian historical practice to date. Drawing upon the experiences and practices of five collaborative or multi-author Canadian history blogs — ActiveHistory.ca, The Otter~La Loutre, Findings/Trouvailles, the Acadiensis Blog, and Borealia — it explores how this activity is changing the ways in which Canadian historians tell stories, publish their research, teach, and serve academic and wider communities. Blogging has encouraged new forms of historical storytelling and the inclusion of underrepresented and marginalized voices in public discussions of Canadian historical narratives. It is being integrated into cycles of academic publication and undergraduate and graduate classrooms. Yet challenges remain with regard to determining the place and value of blogging within standard paradigms of academic labour. As more Canadian historians come to read, write for, and edit historical blogs, however, they will not only help shift the practice of Canadian history inside and outside university campuses, but will also experience the pleasures and rewards of this kind of digital historical work for themselves.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.398
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.214
GPT teacher head0.413
Teacher spread0.200 · 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.

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

Citations2
Published2017
Admission routes2
Has abstractyes

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