The double bind of validation: distant reading and the digital humanities' “trough of disillusionment”
Bibliographic record
Abstract
Abstract The digital humanities (DH) is currently in the phase of the “hype cycle” known as the “trough of disillusionment.” Franco Moretti, perhaps the most prominent practitioner of the most prominent discipline of DH—“distant reading,” the computational analysis of large quantities of literary texts—recently expressed his exasperation with the state of DH, reflecting “our work could have been better” and asking why, “considering the amount of energy, talent, and tools, going into [DH], that we have such difficulty producing great results.” Surveying leading recent work in distant reading by Moretti, Matthew L. Jockers, Laura Mandell, Ryan Heuser, Long Le‐Khac, and Joanna Swafford, this paper provides a twofold explanation to the field's failure to produce “great results.” Both explanations relate to “validation,” the process by which quantitative results are shown to be reliable and trustworthy. Many distant reading projects have produced disappointing results because they have been more interested in validating their tools—showing that their computational methods are able to confirm existing stereotypes—than in pursuing genuine discoveries. Many others, meanwhile, produce provocative results that cannot be meaningfully validated. Although the double bind of validation is real, I propose collaboration and “interdisciplinary adaptation” as promising solutions.
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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 | 0.087 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.013 | 0.131 |
| Scholarly communication | 0.033 | 0.042 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 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".