Bibliographic record
Abstract
From the author’s Canadian-based perspective, this paper investigates the contexts of the politics of postcoloniality in the contemporary period to query the relation between decolonising the state and decolonising the mind as it is currently being theorized in academic circles. Working through a variety of slogans that now limit the productivity of a politics of postcoloniality (the “politics of blame,” strategies of “them and us,” the assumptions behind “speaking truth to power” and “the personal is political”), the paper advocates attention to alternatives that balance critique with imagining otherwise. Arguing that postcolonial politics take place within a larger crisis of politics itself, the paper suggests that foundational assumptions (about citizenship, civility, public and private spheres) embedded within liberation struggles and the academic study of the postcolonial require further clarification within contexts of “unlearning” and the acceptance of “dilemmatic spaces” of engagement. In working through the ideological and institutional constraints facing postcolonial studies today, the paper finds hope in several different engagements with indigeneous cultures and histories that seek to revise the conditions through which political dialogue might take place.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.038 | 0.083 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".