Artificial intelligence as planetary assemblages of coloniality: The new power architecture driving a tiered global data economy
Why this work is in the frame
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Bibliographic record
Abstract
We present a framework for viewing artificial intelligence (AI) as planetary assemblages of coloniality that reproduce dependencies in how it co-constitutes and structures a tiered global data economy. We use assemblage thinking to map the coloniality of power to demonstrate how AI stratifies across knowledge, geographies, and bodies to influence development and economic trajectories, impact workers, reframe domestic industrial policies, and reconfigure the international political economy. Our post-colonial framework unpacks AI through its (1) global, (2) meso, and (3) local layers, and further dissects how these layers are vertically integrated, each with its horizontal dependencies. At (1) the global layer of international political economy maps a new digital bipolarity expressing Sino and American global digital corporations’ strategic and dominant positions in shaping a tiered global data economy. Then, at (2) the meso layer, we have a mosaic of domestic industrial policies that fund, frame markets, and develop AI talent across industries, sectors, and organizations to competitively integrate into AI value chains. Finally, incorporating into these are (3) the localized labor processes and tasks, where workers and users enact various AI-mediated tasks and practices driving further value extraction. We traced how AI is an interlaced system of power that reshapes knowledge, geographies, and bodies into dependencies that reinforce stratifications in developing underdevelopment. This commentary maps the current digital realities by laying out an uneven techno-geoeconomic power architecture driving a tiered global data economy and opening new research avenues to examine AI as planetary assemblages of coloniality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it