Imperial algorithms: Contemporary manifestations of racism and colonialism
Bibliographic record
Abstract
In this special issue, we invited contributions that critically examined issues of imperialism, colonialism, power, justice, etc. to expand the canon of anticolonial scholarship and critical scholarship in community psychology. Our two objectives were: (1) to build on the canon of anticolonial and critical race scholarship to cultivate an empirical and theoretical body of work and conceptual frameworks about racism and colonialism within the field of community psychology and (2) to unpack the different manifestations of racism in society from the lens of community psychology and reflect on the implications of these varied forms of injustice in the contemporary moment. Rooted in African epistemology and methodology (Martin, 2012), we find the concept of the algorithm to serve as a potent metaphor for the ways in which these oppressive structures operate given the prevalence of algorithms in our daily lives and the algorithm is symbolic of the information age and predictive powers that seem to govern society beyond conscious control. In this sense, imperial algorithms are these structures, patterns, processes, and procedures that perpetuate imperialism. These imperial algorithms manifest as neo-colonialism, surveillance, social engineering, carcerality, reality warping of contemporary racism, health disparities exacerbated by COVID-19, and environmental grids of oppression.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".