Beyond the Numbers: Statistical and Data Literacy, Domain Literacy and Supreme Court of Canada Data Analytics
Bibliographic record
Abstract
There is growing interest in using data analytics to identify patterns or trends in the work or behaviour of a court or judge. As more analytics-generated information about courts and judges is pumped into public discourse, questions arise about what it takes for stakeholders — both those producing or generating judicial analytics outputs, and those relying on and attempting to make sense of analytics outputs — to properly engage with that data. In this paper, we identify two related but distinct kinds of literacy required for meaningful and responsible engagement with judicial analytics outputs: (1) data and statistical literacy, that is, familiarity and facility with the basic concepts and methods required to effectively engage in (and with) quantitative research; and (2) domain literacy, which requires users to understand the unique features of the domain or area that an analytics output reflects in order to properly interpret that output with full regard for the nuances of context. Using data from and about the Supreme Court as a case study, we consider the ways that both types of literacy are essential to ensure that judicial analytics outputs further the public’s understanding of the judiciary rather than mislead, confuse or distract.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".