The Inevitability of Teleology: From le Dispositif to Apparatus Theory to Dispositifs Plural
Bibliographic record
Abstract
The article asks how problems of historical method are implied in the question “What Is Left of Apparatus Theory [...] ?”, the topic of the round table for which this paper was first delivered at the IMPACT conference. It goes on to recap the problems with traditional historical approaches : the “from…to” overview, the linear narrative, and the teleological expectation, to name a few. Then, it suggests, following Reinhard Koselleck, that teleologies (like the teleology to end all teleologies – the death of cinema) are unavoidable if one is functioning as a historian-positioned-in-time. Finally, it asks how we are to lay out Baudry and Foucault and trace the “adventures” of the apparatus and apparatus theory when the discourse theory that underwrote their projects critiques the project of “discovering” past events since in the end we are constituting the technology we discover, the same technology that we have said constitutes us.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.073 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".