Counting without Accounting? The Evolution of Identity Technology and Social Justice in Kenya
Bibliographic record
Abstract
Abstract Many low and middle income countries (LMICs) are rolling out digital ID programmes, where the state makes a digital record of a person’s identity, for use in identification and data based decision making. Digital ID programmes have been contested on technical and political grounds. Political issues, such as the potential of digital ID to exclude, discriminate and deny services to certain groups of people, as well as the surveillance capabilities of digital ID data in the hands of national security officers, are more difficult to resolve. This article uses Kenya’s legal history to trace the politics of identity practices, particularly during the colonial and post-colonial periods. The long experiences with identity registration and use in Kenya provides important lessons for many LMICs implementing digital ID for general population registration and social welfare programmes. The article connects three main issues that have been at the core of digital ID contestations - inclusion, digital welfare and power using social justice theory. Using the lenses of social inclusion, social minimums and social power, the paper finds that the unjust colonial origins of Kenya’s registration laws still linger in present laws and practices. As such, digitalisation of identity practices would further marginalise groups that are underrepresented in registration.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".