Decolonizing Displacement Research: Betweener Autoethnography as a Method of Resistance
Bibliographic record
Abstract
Over the past decade, there have been increasing numbers of displaced scholars from the Middle East and Africa who have come under sustained pressures and threats from their governments; only a few of them have been able to relocate to European and North American academia through scholarships and grants.1 Even these temporary solutions for displaced scholars rarely result in sustainable institutional solidarity in the form of permanent teaching or professorial positions. The lack of institutional support, coupled with discriminatory and racialized immigration policies, pushes these few fortunate scholars to either accept exploitative conditions perpetuated by the neoliberal economy or leave academia altogether to support their families. These challenges, along with draconian economic sanctions and restrictions imposed by the US Treasury Department Office of Foreign Assets Control (OFAC) on citizens of countries such as Syria, Sudan, Iran, and Cuba are only a snapshot of what displaced scholars endure on a daily basis while trying to do research, care for their families, and compete with scholars with privileged citizenship status for shrinking opportunities in the academic job market.
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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 | 0.039 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".