Bibliographic record
Abstract
We live in a colonial global economy. Colonialism has always operated through the creation and maintenance of racial hierarchies and exclusions. Financial practices and financial institutions have been as intimately entangled with racial hierarchy, racism and white supremacy as have colonizing states and their bureaucracies. The chapters in this book all share a concern with the entanglement of race, empire and finance in the colonial present. Each and every chapter is organized around an image: a photograph, work of art, map, advert, or diagram. For some, the central concern is the relationship between hostile borders, race, migration, and personal indebtedness. For others, it is the relationship between extractive infrastructure and the transition from European to Chinese investment in Africa. Several chapters are concerned with the settler colonial settings of contemporary Canada, Australia, New Zealand and the USA. Some are historical, some personal, and others based on interviews and ethnographic fieldwork. We have chosen to organize this book around a set of images to facilitate interdisciplinary dialogue and learning. We invite you to explore your own connections as you unpick and identify the manifold entangled legacies which make an appearance in this collection.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.430 | 0.275 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".