ty_fairness: Fairness diagnostics for prior-informed routing and scoring

In throughyear: Linking, Routing and Fairness for Through-Year Assessment

Fairness diagnostics for prior-informed routing and scoring

Description

For each policy and group, reports routing accuracy, the rate of being routed to an easier module than the one most informative at the student's true ability (the "locked into an easier path" concern), and bias and RMSE of the reported score. Bias for a group that the prior systematically under-predicts (late bloomers, students whose growth accelerated after the last interim) is the central fairness signal.

Usage

ty_fairness(policies, groups)

Arguments

policies	A 'ty_policies' object.
groups	Named list of logical vectors (one element per examinee).

Value

Data frame: 'policy', 'group', and the metrics of 'summary()'.

Examples

sim <- ty_simulate(n_calibration = 300, n_operational = 300, seed = 1)
op <- sim[sim$cohort == "operational", ]
prior <- predict(ty_link(sim), op)
pol <- ty_policies(ty_mst_default(), op$theta_S, prior, seed = 1)
fair <- ty_fairness(pol, list(late = op$late, fast = op$fast))
fair[fair$group == "fast", c("policy", "routed_too_easy", "bias")]

throughyear documentation built on Oct. 8, 2026, 5:07 p.m.