Decolonizing <scp>IS</scp> through <i>tecnologia social</i>: Fostering epistemic plurality in the design of solidarity cryptocurrency in Latin America
Bibliographic record
Abstract
Abstract Westernised paradigms dominate the information systems (IS) field, often overshadowing alternative epistemologies. This study challenges the prevailing hegemonic view and contributes to the decolonization of IS research and practice by proposing a Latin American and decolonial approach to technological development that emphasises community centrality and epistemic justice through recognition of local knowledges and Indigenous traditions. Using design ethnography, we follow the development of a solidarity cryptocurrency in a Brazilian favela. Our paper offers two key contributions. By introducing tecnologia social, an underrepresented perspective in IS, we highlight ecology of knowledges, centrality of the local and decolonial reconfiguration as principles that can enrich the understanding of IS projects from a decolonial perspective. In addition, we propose a new concept—epistemic dialogical tension—as a process wherein different epistemologies coexist and accommodate each other, encouraging a dynamic interplay of distinct human experiences and worldviews. It offers new paths to IS scholars and practitioners in navigating the complexities of epistemic plurality. We argue that tecnologia social and epistemic dialogical tension provide fertile ground for developing reimagined, decolonized approaches where multiple epistemologies can coexist, favouring the often‐silenced communities they are intended to benefit.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| 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".