Digital Humanities and Distributed Cognition: From a Lack of Theory to its Visual Augmentation
Bibliographic record
Abstract
Digital humanists have often been criticized as too technology-driven and for a lack of theoretical work. In this paper, we discuss theories from Cognitive Science on the *extended mind*, which provide a productive framework to theorize the use of tools and technologies for the sake of cognitive self-enhancement. Viewed through this lens, humans continuously self-amplify their natural cognitive resources and processes by extending and offloading them to interactions with artifacts and other individuals in their environment. Concepts of extended cognition further sharpen the focus on multiple types of distribution: from the outlined internal-external distribution to the propositional-visual distribution of cognition, but also for the multi-instrumental distribution across multiple types of tools and tool specialist. All these aspects are relevant for future debates about a "theory gap" in the digital humanities: DH mainly builds external, technological tools, while traditional humanities develop conceptual tools---including theories---to enable and enhance the study of complex cultural phenomena. Notwithstanding the value of confrontational discussions, we argue for the benefits of understanding the strengths and limitations of instruments on both sides---and for working toward future synergies and ecologies of the humanities' tools and minds. In this regard, we show how visualization-based DH tools might might play a major role in closing the comprehensibility gap of traditional theories in the arts and humanities.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.050 |
| Scholarly communication | 0.014 | 0.033 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".