Book Review—The Right to Research: Historical Narratives by Refugee and Global South Researchers. By Kate Reed and Marcia C. Schenck, eds
Bibliographic record
Abstract
The Right to Research: Historical Narratives by Refugee and Global South Researchers is essential reading. Much of the work that is being taught, cited, and used in advocacy for and on behalf of refugees are written by scholars from the Global North. Even though many migrants are racialized and from the Global South, the scholarship that receives the most attention is that of scholars who have no experience with the challenges their research subjects face. This is changing, but there is a stickiness to ideas about what is research that meets the standards of peer review and how research should be done. This book builds upon decolonizing, anti-racist, and critical feminist and race thought that has long embraced multiple and diverse forms of knowledge production. Ironically, refugee and migration studies have taken much longer to challenge norms and assumptions that underpin research in this area. Nine essays make up this collection. The authors have liminal or no legal status, some dependent on organizations or NGOs for support, have diverse educational and employment backgrounds, and were from a variety of countries. The works show less emphasis on the privileged ability to use research resources from libraries, the internet, and workshops and instead showcased conditions of production outside the traditional bounds of academia that were non-textual and focused on oral narratives. The work in this book does as it states, ‘create[s] new archives’ and ‘record[s] oral histories that can speak to processes, experiences, and subjectivities that no amount of sleuthing in colonial (or postcolonial state, or NGO) archives, however skilled, could uncover’.1 This book disturbs how refugee research thus far has been done by upturning the construction of the refugee as subject and instead allowing migrants to identify and act as researchers in their own right. As articulated, ‘the right to research is both the ability to systematically inquire into the unknown and the ability to share one’s findings and be taken seriously as a producer and bearer of knowledge—something that has historically been denied to many refugees and other people on the move’.2
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.023 |
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".