The Computational Fallacy: A New Model for Understanding the Role of Computers in Humanities
Bibliographic record
Abstract
The paper tries to counter some misassumptions about the computer and computation especially in relation to humanities and human behavior and they amount to what the author calls “the computational fallacy. The paper discusses a number of points such as the physiology of the computer, the Etymology of basic terms in the field, and the current approaches especially in mainstream digital humanities which reduce computation to a set of “tools”. The paper then proceeds to discuss some counter arguments such as rethinking the notion of programmability which should substitute “calculation” as the core of computation, considering the transformative nature of the computer and its media using the ideas of some theorists like Manovich and Drucker, and some new approaches that view computation differently like Computational Thinking, Algorithmic criticism, and Speculative computing.Cet article essaie de contrer quelques suppositions erronées sur l’ordinateur et la computation, plus particulièrement leur relation aux sciences humaines et au comportement humain qui équivaut à ce que l’auteur appelle « the computational fallacy » ou l’erreur computationnelle. Cet article aborde de nombreux points, tels que la physiologie de l’ordinateur, l’étymologie de termes de base dans ce domaine, ainsi que les approches courantes, particulièrement les approches dominantes dans les humanités numériques qui réduisent la computation comme un ensemble « d’outils ». Ensuite, l’article poursuit en discutant les contre-arguments comme les nouvelles réflexions des notions de programmation qui devraient substituer le calcul au cœur de la computation, considérant la nature transformante de l’ordinateur et ses médias utilisant les idées de quelques théoristes dont Manovich et Drucker, et quelques nouvelles approches qui voient la computation différemment comme « Computational Thinking » ou la pensée computationnelle, « Algorithmic criticism » ou la critique algorithmique et « Speculative computing » ou la computation spéculative.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".