Bibliographic record
Abstract
The essay reads Edward Said’s seminal work Orientalism through Sianne Ngai’s category of the “stuplime”. Unlike the noble emotions elicited by the Kantian sublime, the stuplime causes the ugly feelings of tedium, excitation, fatigue. In the Kantian sublime, viewing subjects experience an overwhelming feeling which impresses on them their own inadequacy; in my reading of stuplime orientalism, the “Orient” becomes the object of this “inadequacy”. This shift from inadequate subjects to inadequate objects is one manifestation of the political valence of the stuplime, a means by which control is maintained both of the “Orient” as a colonial project and of the orientalist’s mind. As Ngai argues, however, the political potential of ugly feelings is always ambivalent: stuplime orientalism can therefore also be coopted through curation of stuplime orientalist detail, which I deploy in the second half of this essay to read Jade Snow Wong’s memoir, Fifth Chinese Daughter. Resisting the urge to summarize Jade Snow Wong’s exhaustively descriptive rice passages, I delineate an alternative interpretation to the prevailing view that she merely self-orientalizes, instead suggesting a reading that engages histories of denigrated racial formation and labour precisely through, and not in spite of, stuplime orientalist detail.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".