Forced Displacement and Racialization: The Colombian Experience
Bibliographic record
Abstract
This thesis compares the differential processes of racialization from a Colombian perspective experienced by three groups of displaced migrants in the global North and South. First, internally displaced persons (IDPs) who have been forced to move to the Coffee Region in Colombia after leaving their homes in rural regions between 2000 and 2015. Second, Colombian refugees who had similarly sought asylum in Toronto, Canada, and who migrated between 1997 and 2004. Third, Venezuelan migrants who arrived in the Coffee Region in Colombia between 2014 and 2018 due to the deteriorating living conditions and crisis in Venezuela. This research contributes to further theoretical debates on critical geographies of race, postcolonial geography, and urban geography in relation to forced migration. The objective of this research is to question understandings of race and racism, particularly how space and mobility affect the dynamics of racialization through such diverse experiences of forced displacement. The main argument of this research is that the process of forced displacement (as experienced by the Colombian IDPs and Venezuelan migrants to the Coffee Region in Colombia, and for the Colombian refugees to Toronto), results in spatialities of racialization. While escaping violence and economic hardship, forced migrants are subjected to oppressive and exclusionary processes that make them vulnerable to systemic racism and microaggressions. This comparative research uses a combination of qualitative methodologies, including in-depth semi-structured interviews, participant observation, field diary, and policy and document reviews. The research reveals that despite different experiences of internal displacement or transnational migration, spatial processes of racialization present similar dynamics of white supremacy as the dominant racial ideology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".