"Any Search for an Origin is Hysterical": Summoning the Ghost of J.L. Austin
Bibliographic record
Abstract
As the father of the concept of the “performative utterance”, British philosopher of language J.L. Austin is regularly cast as the point of origin in a genealogy tracing the influence of linguistic theory on performance theory. Based on extensive research into Austin’s writing praxis, this paper demonstrates that the philosopher produced and disseminated his research orally, dialogically, and pedagogically through contexts that privileged the inter-subjective exchange. It frames Austin’s self-described practice of “linguistic phenomenology” as a pragmatic one in which philosophy is context. I demonstrate that this mode of “doing” or “performing” philosophy is also at play within the dramaturgy of Austin’s texts, which restage his thought processes and invite his readers to become spectators to the dramatization of his ideas. My analysis offers up a portrait of a J.L. Austin who enacted his philosophy about the performative utterance in a performative manner. In so doing, it exposes the inaugural texts about performativity as hybrid objects that trouble the concepts of “authorship” and “origin”. It also shows that these texts are infected, at their inception, by parasites, by literature, by the Other, and by the ghosts that Austin tried so hard to exorcise, yet that—on some level of consciousness—he simultaneously allowed to haunt his philosophical voice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".