"To think a world without thought": Negotiating Speculative Realism in a Digital Humanities Practice
Bibliographic record
Abstract
In John Napier’s classic study of the structure, function, and behavior of the human hand, he states that "[i]magination is basic to tool-making. All human-made tools start off as chunks of undifferentiated material, which are then shaped according to some cerebral blueprint of what is required" (1993, 99). These words can be productively repurposed to describe the digital humanities practice of tool making, tool use, and modelling. After all, making, shaping, and using tools for analysis, archiving, and visualisation has become central to humanistic research methods. Napier's "cerebral blueprint" blends imaginative and technical ways of making that emulates how digital humanists seek out new insights alongside technological development. As increasing numbers of digital humanists hone their literacy of the programming languages to build and remake tools, I argue that the means by which we describe this particular philosophy of tool use will become an increasingly thorny issue that may even hinder the usefulness of the knowledge produced by the digital humanities. Therefore, digital humanists will need to find ways to negotiate this role in the humanities and better define their critical agency within the history of epistemology. Additionally, a collaborative development of tools requires a theoretical framework that is critical of the value of data derived from literature in a purely instrumental way and is able to redefine research artifacts in the humanities to include digital tools. Dans son étude classique de la structure, de la fonction et du comportement de la main humaine, John Napier affirme que "l’imagination est à la base de la fabrication des outils. Tous les outils fabriqués par l’humain sont, au départ, des morceaux de matériaux indifférenciés, qui sont ensuite façonnés selon un certain schéma cérébral de ce qui est requis" (1993, 99). Cette affirmation peut être réutilisée de façon productive pour décrire la fabrication, l’utilisation et le modelage des outils, selon la pratique des humanités numériques. Après tout, fabriquer, façonner et utiliser des outils pour l’analyse, l’archivage et la visualisation est maintenant au cœur des méthodes de recherche en sciences humaines. Le "schéma cérébral" de Napier combine des moyens imaginatifs et techniques de fabrication qui imitent la façon dont les humanistes numériques recherchent de nouvelles idées en même temps que le développement technologique. À mesure qu’un nombre de plus en plus élevé d’humanistes numériques perfectionnent leurs connaissances des langages de programmation pour bâtir et refaire des outils, j’argumente que les moyens par lesquels nous décrivons cette philosophie particulière d’utilisation des outils deviendront une question de plus en plus épineuse qui pourrait même gêner l’utilité des connaissances produites par les humanités numériques. Par conséquent, les humanistes numériques devront trouver des moyens de négocier ce rôle dans les humanités et de mieux définir leur mandat critique au sein de l’histoire épistémologique. De plus, un développement collaboratif d’outils nécessite un cadre d’applications théorique qui est essentiel à la valeur des données dérivées de la littérature de façon purement instrumentale, et qui est capable de redéfinir les artéfacts de recherche dans les humanités afin d’y inclure les outils numériques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.028 | 0.216 |
| Scholarly communication | 0.035 | 0.051 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 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".