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Record W2290561748 · doi:10.7202/1032846ar

A Plea for Design: Historians, Digital Platforms, and the Mindful Dissemination of Content and Concepts

2015· article· en· W2290561748 on OpenAlexvenueaboutno aff
John Bonnett

Bibliographic record

VenueJournal of the Canadian Historical Association · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativePleaCraftWorkflowComputer scienceDigitizationDigital contentMandateWorld Wide WebVisual artsLiteraturePolitical scienceLawArt

Abstract

fetched live from OpenAlex

One of the most significant attributes of digital computation is that it has disrupted extant work practices in multiple disciplines, including history. In this contribution, I argue that far from being a trend that should be resisted, it in fact should be encouraged. Computation is presenting historians with novel opportunities to express, analyze, and teach the past, but that potential will only be realized if scholars assume a new research mandate, that of design. For many historians, such a research agenda is likely to seem strange, if not beyond the pale. There are tasks that fall properly within the domain of the Historian’s Craft and the design of workflows for digital platforms and expressive forms for digital narratives are not among them. In Section One, via the writings of Harold Innis, I make the case that a preoccupation with design is in fact very much part of Canada’s historiographic tradition. In Section Two, I present an environmental scan of emerging technologies, and suggest that now is an opportune time to revive Innis’ preoccupation with design. In the following two sections, I present the StructureMorph Project, a case study showing how historians can leverage the properties of digital form to realize their expressive, narrative, and attestive needs in the digital, virtual worlds that will become increasingly important platforms for representing, disseminating, and interpreting the past.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.224
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.081
GPT teacher head0.232
Teacher spread0.151 · 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

Citations2
Published2015
Admission routes2
Has abstractyes

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