Bibliographic record
Abstract
In the last two decades, it has become an accepted principle in psychology that the sense of self depends on autobiography. Without the ability to organize experience through narration, arguably one cannot have a coherent self. If one’s self is indeed dependent on autobiography, then that same narratively constituted fictive self is especially susceptible to erosion and erasure through memory loss and narrative disability. In Judith Thompson’s Perfect Pie, Patsy is such a character, suffering from trauma-related amnesia that inhibits the realization of a full extended self. Although on the one hand, Patsy’s status as a fictive character leaves her vulnerable to the power of words to undermine the stability of a narratively generated self, on the other hand, her ontological situation as a character born in words also grants her significant power to wield that same performative power to write her self. This article will examine the dialogic self-authoring strategy that Patsy adopts to generate multi-vocal autobiography, weaving thematically associated stories across disparate nested fictional worlds. Ultimately, Patsy’s potential cure lies not in the revelation of an objectively-verifiable historical truth but rather in the pie-making, theatre-making, self-making process of continued reiterative performance.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".