Bibliographic record
Abstract
Author Sonja Boon’s heritage is complicated. Although she has lived in Canada for more than thirty years, she was born in the UK to a Surinamese mother and a Dutch father. Boon’s family history spans five continents: Europe, Africa, Southeast Asia, South America, and North America. Despite her complex and multi-layered background, she has often omitted her full heritage, replying “I’m Dutch-Canadian” to anyone who asks about her identity. An invitation to join a family tree project inspired a journey to the heart of the histories that have shaped her identity. It was an opportunity to answer the two questions that have dogged her over the years: Where does she belong? And who does she belong to? Boon’s archival research—in Suriname, the Netherlands, the UK, and Canada—brings her opportunities to reflect on the possibilities and limitations of the archives themselves, the tangliness of oceanic migration, histories, the meaning of legacy, music, love, freedom, memory, ruin, and imagination. Ultimately, she reflected on the relevance of our past to understanding our present. Deeply informed by archival research and current scholarship, but written as a reflective and intimate memoir, What the Oceans Remember addresses current issues in migration, identity, belonging, and history through an interrogation of race, ethnicity, gender, archives and memory. More importantly, it addresses the relevance of our past to understanding our present. It shows the multiplicity of identities and origins that can shape the way we understand our histories and our own selves.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".