Translating Law and Code in Government: Algorithmic Decisions and Their Legal Effects in Canada
Bibliographic record
Abstract
This article analyzes the translation of law into computer code and the use of automated decision-making systems in government to make legal distinctions. Specifically, how are algorithmic decisions tied to law, and what happens when legal effects are mediated through technologies? The sociology of translation and Bruno Latour's theory of law, as elaborated by Kyle McGee, provides the means to study associations between law and technology. I trace how the force of law can be extended when mediated through computer systems and analyze the associations of law and technology in Canada's government, through projects exemplifying the shift to "code-driven law." These include the translation of "rules-as-code," and several of the sociotechnical systems governing Canada's borders, demonstrating how design choices in government digital services inevitably shape the outcomes of public policy and can have legal effects. While Latour's legal scholarship avoided traditional questions of legitimacy, a key consideration for automated government systems is how legitimacy is constructed and contested. For rules-as-code, legitimate algorithmic outcomes should be traceable to law, but existing government systems commonly maintain legitimacy by identifying a human actor "in-the-loop" as the ultimate decision-maker, thereby obscuring how thoroughly imbricated human and algorithmic agency are in contemporary governance.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.028 | 0.029 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".