12. The Weight of The Digital: Experiencing Infrastructure with InfraVU
Bibliographic record
Abstract
The following chapter by Ted Dawson explores the environmental entanglements of the digital humanities, considering the imbrication of digitally-driven attempts to confront environmental crisis with the contributions of digital technologies to that very crisis. The chapter centers on a case study of the InfraVU project undertaken in 2016-2017 at the Vanderbilt University Center for Digital Humanities, a project that sought to draw attention to the infrastructure supporting digital humanities (DH) at Vanderbilt. Dawson first considers the experience and concealment of infrastructure in contemporary life, and especially at the university. He then moves into a fuller description of the InfraVU project itself, showing how the development of the project exploited a productive tension between making and thinking which is central to so much DH work, and which can be understood as a specific inflection of the larger tension between understanding digital culture and digitally understanding culture. In addressing that tension, the InfraVU project demonstrates how digital humanists can use computational methods to think through environmental issues, while also reflecting critically on how that technology is itself implicated in environmental issues. The chapter concludes by foregrounding the role of the arts and humanities in ecocritical digital humanities (EcoDH).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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 teacher head, 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".