Neocolonial Digitality: Analyzing Digital Legal Databases Using Legal Pluralism
Bibliographic record
Abstract
Abstract A prevalent assumption is that digital legal databases generate an exhaustive and inclusive archive for academics and legal professionals to use for gathering information. Bridging theories and methods from digital media studies and legal anthropology, I challenge this assumption and demonstrate how digitizing law is a politicized process that is tied to legacies of colonialism and modern epistemic frameworks of law and justice. Employing the concept of legal pluralism, I conduct a comparative study of urban secular state courts and rural Islamic/customary non-state courts (shalish) in Bangladesh to show how the construction of digital legal databases distorts and erases alternate frameworks of law and women’s socio-legal experiences. I discuss two significant use of digital legal databases to highlight why it is important to study the gaps and prejudices: (1) they are central to generating new forms of archives—digital archives; (2) they provide the data sets to help train artificial intelligence and influence automated outputs. I develop the term “neocolonial digitality” to explain how power related to legacies of colonialism and other forms of discrimination are embedded in the digitizing process. This concept also holds space for the newer forms of hierarchies, exclusions, and power structures that digitality permits, focusing on the particular harms marginalized communities encounter in the Global South.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".