Tools · People analytics
Exemplar Classifier
Classify any free-text response against labeled exemplars — predicted class + calibrated confidence.
How it works
Public-domain implementation of US20180025303A1 (abandoned patent). Two channels blended per the patent's multi-dimensional framing: (1) a semantic embedding channel (key-gated — real provider embeddings with OPENAI_API_KEY, deterministic lexical fallback without, flagged `degraded`, never silently faked) and (2) a psycholinguistic channel of textbook constructs — Weintraub's 14 speech categories (1964) + Mehrabian's psychological-distance/nonimmediacy (1966), all deterministic/key-free. Nearest-prototype clustering against the labeled exemplars; softmax-calibrated confidence; full feature + nearest-exemplar explainability.
You bring
{ response, exemplars? | exemplarSetId?, semanticWeight?, topK? } — inline labeled exemplars OR a built-in set id ('interview-orientation')
You get
{ predictedClass, confidence, margin, classScores[], nearestExemplars[], features{weintraub,mehrabian}, embedding{mode,degraded} }
Use it for
- →Score a constructed interview response against ownership-vs-deflecting exemplars (built-in 'interview-orientation')
- →Bring your own validated, labeled exemplars → segment open-ended survey/assessment responses with provenance
- →Inspect the Weintraub/Mehrabian profile of any text — self-reference, hedging, psychological distance — as standalone features
See it work
example outputA candidate's constructed interview answer scored against the built-in 'interview-orientation' exemplar set (ownership vs deflecting).
Exemplar Classification — interview-orientation
Response scored: "When our launch slipped, I owned it. I rewrote the rollout plan myself, called the two biggest customers, and shipped the fix that week. I'd rather catch a problem early and answer for it."
Verdict
- Predicted class:
ownership - Confidence: 0.86 (softmax-calibrated)
- Margin over runner-up: 0.72
Class scores
| Class | Blended | Semantic | Psycholinguistic | Support |
|---|---|---|---|---|
| ownership | 0.86 | 0.84 | 0.89 | 4 exemplars |
| deflecting | 0.14 | 0.16 | 0.11 | 4 exemplars |
Nearest exemplars (explainability)
own-2(ownership, sim 0.81) — "When the project stalled, I stepped in. I rewrote the test plan… and I shipped the fix the same week."own-1(ownership, sim 0.78) — "I took responsibility for the missed deadline. I called the client myself…"own-4(ownership, sim 0.74) — "I volunteered to lead the migration… I am accountable for that decision."
Psycholinguistic profile (query)
- Word count: 41
- Weintraub (rate /1,000 words): high
iself-reference, highdirect_references, lowqualifiers/retractors— agentic, un-hedged. - Mehrabian non-immediacy: 0.18 (low distance) — active voice, first-person, present-framed; little objectification or passivity.
The psycholinguistic channel agrees with the semantic channel: first-person agency and concrete action, the structural signature of the ownership exemplars rather than the impersonal/passive deflecting ones.
Embedding channel
- Mode:
api· modeltext-embedding-3-small· semanticWeight 0.6 ·degraded: false(real provider embeddings; the lexical fallback would set this true).
Provenance: service exemplar-classifier · patent US20180025303A1 (abandoned → public domain) · method weintraub-1964 + mehrabian-1966 + embedding-exemplar-match · exemplarSet interview-orientation. Built-in set = illustrative demonstration labels, not validated psychometric norms — bring your own labeled exemplars for a clinical-grade signal.
Run it on your data
Call it on your own inputs — over the API, or hand it to your AI agent via MCP. Discovery is open; running it is metered.