Crossing borderlands: composition and postcolonial studies
Bibliographic record
Abstract
Composition and postcolonial studies : an introduction / Andrea A. Lunsford and Lahoucine Ouzgane -- Composing postcolonial studies / Min-Zhan Lu -- Toward a Mestiza rhetoric : Gloria Anzaldua on composition and postcoloniality / Andrea A. Lunsford -- Terms of engagement : postcolonialism, transnationalism, and composition studies / Deepika Bahri -- Encountering the other : postcolonial theory and composition scholarship / Gary Olson -- Pratt and pratfalls : revisioning contact zones / R. Mark Hall and Mary Rosner -- Beside ourselves : rhetoric and representation in postcolonial feminist writing / Susan Jarratt -- Postcolonial transformations in Canadian Inuit testimonio / Martin Behr -- (Im)migrant crossings / Aneil Rallin -- Resisting writing : reflections on the postcolonial factor in the writing class / David Dzaka -- Arts of the U.S. Mexico contact zone / Jaime Armin Mejia -- Hybridity : a lens for understanding Mestizo/a writers / Louise Rodriguez Connal -- The politics of location : Using flare-ups to spark "reflexive dialogue" in the ever-changing classroom text / Pamela Gay -- The new literacy/orality debates : ebonics and the redefinition of literacy in multicultural settings / Jan Swearingen.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".