Bibliographic record
Abstract
This paper begins by posing the following questions: Who are the subaltern in the global present? What refigurations has the concept of subalternity undergone since its inaugural use in Antonio Gramsci’s writing? What does a genealogical account of such revisions and discontinuities suggest? Can the subaltern speak or be heard in a digital world? Why translate for the subaltern? If translation is an impossible necessity, what risks and pitfalls are encountered in translation for the subaltern? What potential does a politically empowering ethics of translation offer for surmounting such obstacles? Using these questions as a point of departure, this paper proceeds to explore how in the age of digital media communications the previously colonized or subalternized are further hegemonized, and what mechanisms are involved when digital imperialism is further marginalizing and silencing the subaltern. If the history of colonialism has witnessed translation being manipulated as a vehicle to achieve and maintain domination and control, the paper argues, then translation can also serve as a powerful site or tool for repairing social injustice and epistemic or representational violence against the subaltern, and therefore help enable the subaltern to speak for themselves and be heard sympathetically and respectfully.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".