Bibliographic record
Abstract
This study investigates how the desire to ascertain a sense of place for Black, Caribbean diasporic subjects living on Indigenous lands can be pursued through respectful engagement with Indigenous literary art. Although returning to our own ancestral homes may not be possible, I argue, as a diasporic person myself, that it is possible for diasporic subjects to find meaning and belonging on the Indigenous lands of Aotearoa, New Zealand and what is now known as Canada through analysis of works like the bone people by Māori author Keri Hulme and Monkey Beach by Haisla author Eden Robinson. Drawing upon each text’s use of narrative, incorporation of Indigenous language, and depiction of tradition, I develop frameworks towards finding belonging whilst respecting Māori and Haisla lifeways. Both personal experience and textual analysis colour this research, which was spurred by a longing for place as a subject of the Black, Caribbean diaspora. This paper argues that humble engagement with Māori and Haisla knowledges, as elicited by Hulme and Robinson’s novels, can contribute to a greater sense of belonging for such diasporic subjects to decolonizing ends. The paper focuses on place as a fluid concept, the importance of humility when engaging with Indigenous knowledge and practices, and the acceptance of Indigenous education without expectation of mastery. Belonging is not a rigid concept, and this paper is not a guidebook, but a launching pad towards a lifetime of learning, unlearning, change and acceptance.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.025 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 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".