Gdzie są moje granice? O postkolonializmie w literaturze
Bibliographic record
Abstract
The experience of colonization and the challenges of a post-colonial world have produced an explosion of new writing in English. This diverse and powerful body of literature has established a specific practice of post-colonial writing in cultures as various as India, Australia, the West Indies and Canada, and has challenged both the traditional canon and dominant ideas of literature and culture. The Empire Writes Back was the first major theoretical account of a wide range of post-colonial texts and their relation to the larger issues of post-colonial culture, and remains one of the most significant works published in this field. The authors, three leading figures in post-colonial studies, open up debates about the interrelationships of post-colonial literatures, investigate the powerful forces acting on language in the post-colonial text, and show how these texts constitute a radical critique of Eurocentric notions of literature and language. This book is brilliant not only for its incisive analysis, but for its accessibility for readers new to the field. Now with an additional chapter and an updated bibliography, The Empire Writes Back is essential for contemporary post-colonial studies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".