From the Archive to the Computer: Michel Foucault and the Digital Humanities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Michel Foucault famously introduced the method of “discourse analysis” in the humanities, especially in historiography. In his _Archaeology of Knowledge_, originally published in 1969, in particular, Foucault argues for making the history of knowledge the object of discourse analyses. In the context of the current surge of interest in discourse analysis in the field of computer science, however, there are hardly any references to Foucault, partly because he never defined a methodological process that could be operationalized. Nonetheless we argue for re-reading the _Archaeology of Knowledge_ in the context of computer science and the digital humanities. As a matter of fact, there are considerable affinities between Foucault’s search for the regularities of discourse and current projects dealing with the digitization of texts, their indexing, distributional features, stylometry, etc. We show that these projects were already quite prominent in Foucault’s day, to the point that historian Emmanuel Le Roy Ladurie could assert, in 1968, that “the future historian will be a programmer.” A year later, Foucault’s _Archaeology of Knowledge_ actively responded and constructively took up the challenge – which, given the recent advances in machine learning and computational linguistics, strikes us as a crucial move today.
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.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it