Bibliographic record
Abstract
In this article, I discuss two lyric poems—Mary Robinson’s “The Lascar” and William Wordsworth’s “Gipsies”—to argue that they engage with what could be dubbed “para-lyric.” Despite presenting itself in the lyric form, this mode critiques the form, specifically the narratable interiority of liberal subjectivity and the disembodied abstraction of voice. Para-lyric functions in this zone of inefficient linguistic communication, forcing the racialized bodies and their alternative ways of being to be brought back into the scene. In place of liberal personhood, which is deeply entangled with the lyric form, the “para-lyric” offers another version of personhood that could function with the paradoxical recognition of opacity, performativity, and “refusals” to enter the circuit of circulation that the burgeoning colonial-capitalist set-up mandates. I choose two lyrics written by “English” poets to demonstrate that “Asian” performance, in being unable to engage in language across racialized lines of sympathy and recognition, manages to “affect” the English and even disturb them. The lack of a readily available English framework to make sense of this “affect” is what adds to the performance’s potential to offer fugitive lines out of the liberal order and imagine new ways of engaging the racialized Other.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".