A Plea for Design: Historians, Digital Platforms, and the Mindful Dissemination of Content and Concepts
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.108 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".