**Case:** Radiologist at a community hospital who adopts an AI triage tool for chest CT reading.

**Primary quadrant:** Shunned expertise (adoption stance, locus = others): colleagues and patients stigmatise the AI adoption as evidence of reduced professional worth. Second judge reads Slopped (self-doubt), reflecting genuine dual-locus ambiguity in the case.

**Mirror quadrant:** Outdated — had they refused the tool, the same institutional environment would likely stigmatise resistance to modernisation instead.

**Verified evidence:** 5/5 quotes confirmed verbatim, including a colleague's challenge ("If the software reads it first, what exactly are we paying you for?") and the department override-rate dashboard as an institutional stigma mechanism.

**Transitions:** 1 of 2 transitions relabelled from "paper" to "extrapolation" by deterministic check; only Slopped → Shunned and Outdated → Slopped are described in the paper.

**Corpus reliability:** Two independent LLM judges (Haiku 4.5 and Sonnet 4.6) coded 36 synthetic comments; Cohen's κ = 0.93 [95% CI 0.82, 1.00]. Per-axis κ: stance 0.89, locus 0.96. Only 2 of 36 disagreements. The 2×2 appears reliably codable from this synthetic corpus.