Use cases· Last updated 2026-09-21

Moderation confidence thresholds with Jev

UGC needs a category, a severity, and an allow/review/remove decision. Jev scores the text you provide against your policy excerpt. Code enforces.

This unofficial page is the confidence thresholds slice of the content moderation pack. Intent: apply the Jev (TypeSafe System One) decision model to content moderation confidence thresholds. Primary search language: Moderation Jev confidence thresholds. Confirm patterns on docs.typesafe.ai. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.

Independent angle (cover ≠ clone): Policy-as-criteria + confidence abort + human pack — not a clone of a moderation-API landing page or rival recipe IA.

Moderation use-case context

Thresholds turn content moderation answers into act / review / abstain. They are product policy, not a hyperparameter TypeSafe ships. Official 0.5 / 0.9 sketches are illustrations. This slice also carries the false-reject discussion: over-gating content moderation hides calibration.

Hub: Use cases. Compare, when the other tool is the real job: moderation APIs.

Confidence Thresholds inputs

You need (1) pinned answers on a frozen contract and (2) labels for keep / review / remove gold from trained mods, plus category gold. State shape:

{
  "post": { "id": "p-209", "text": "…", "locale": "en" },
  "policy": { "hate": "…", "spam": "…", "illegal": "…" },
  "author": { "strikes": 1, "age_gate": "18+" }
}

Decision signals and actions

FLOORS = {
    "keep_visible": 0.70,      # illustrations — replace
    "remove_or_ban": 0.92,
}
NOUL_TAU = 0.75  # for allow

def allow(ans, action):
    return ans.confidence >= FLOORS[action]

Do not treat a Noul of 0.5 as a “medium” content moderation score — it means yes and no are equally likely. Conjunctions stay in your code.

Guardrails and escalation

TypeSafe’s confidence-gated examples use a lower bar for recoverable reads than for irreversible actions. Those numbers are illustrations. For content moderation, treat remove_or_ban as the high bar (removing content or issuing a ban). Tune on labels — see offline evaluation.

Band around 0.5 on allow always reviews. Do not copy 0.75 onto Choice confidence.

Evaluation and rollout notes

• False-remove (voice suppression) vs false-keep (harm) at your floors
• Override rate from humans — if high, criteria or τ are wrong
• Drift after policy-text edits (replay, do not “feel” it)

Fit loop: pin jev-1.13.0 → replay → plot error vs confidence → pick floors where auto-act error ≤ your SLA. Pin jev-1.13.0 (the versioned id) after you fit thresholds. jev-latest and the marketing line jev-1.13 can move. Log the response model. TypeSafe’s published list price for jev-1.13 is $0.042 per million input tokens (vendor claim — confirm on the models page); output tokens are free on that same page. Unused distractors still bill as input.

Official Python and JavaScript SDKs read TYPESAFE_API_KEY and retry documented 429/529. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.

Pack map

FAQ

Should remove_or_ban use 0.9 everywhere? No. Over-gating hides calibration and dumps the queue on humans. Fit per action.

Can I reuse a Noul τ as Choice confidence? No. Jaggedness: they are not interchangeable. See confidence.

Where is the rest of the Moderation pack? Start with Moderation decision workflow and Moderation human handoff. Cluster hub: Use cases.

Should we replace our moderation vendor with Jev? Only after a labeled bake-off you run. This page does not publish one. See Jev vs moderation APIs.

Can Jev moderate images? Not directly. State is text. Run a vision system, put labels/transcripts in state, then ask typed questions.

What this page does not claim

• Not a trust-and-safety certification.
• No published precision/recall.
• Not official TypeSafe.
• Official TypeSafe status, or that jev.pro issues API keys.
• That a schema-constrained answer is automatically factually correct.

Disclaimer

This is an independent unofficial site and is not affiliated with TypeSafe AI; official documentation is available at https://docs.typesafe.ai.

Primary documentation: https://docs.typesafe.ai. Hub: Use cases.