“Can You Tell the Rhetorical Difference?”: Foraging and Fodder in Rita Wong’s Forage
Bibliographic record
Abstract
In forage, Rita Wong explores the subversion and lexicon of “familiar” cultural narratives—that is, status quo stories—with its less-familiar affects. Calling upon her skillful use of poetics, Wong challenges material interconnectedness by revealing how neoliberal ideology supports and inextricably links status quo stories to the socio-political and the cultural; that is, identity is not only surrounded but also rendered by constructs of commodification that is determined through language and physical bodies. In this essay, invoking protean assemblages of mattering in relation to identity, I explore how “foraging” and “fodder” are in tension in Wong’s collection, highlighting the search for (intellectual) sustenance, and yet how being caught within a capitalist system and its deployment of “status quo stories” is used in turn as “fodder” for the functioning of neoliberal machinery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.027 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".