R0029/2026-03-27/Q005 — ACH Matrix¶
Matrix¶
| H1: Widespread, all contexts | H2: Limited/anecdotal | H3: Documented in academic/workplace, sparse for SE | |
|---|---|---|---|
| SRC01-E01: 57% workers hide AI use | ++ | -- | ++ |
| SRC02-E01: 22% students admit use despite belief it's cheating | ++ | -- | ++ |
| SRC03-E01: 7,000 UK formal cases | + | -- | + |
| SRC04-E01: Cheating rates unchanged 60-70% | N/A | - | + |
Legend:
- ++ Strongly supports
- + Supports
- -- Strongly contradicts
- - Contradicts
- N/A Not applicable to this hypothesis
Diagnosticity Analysis¶
Most Diagnostic Evidence¶
| Evidence ID | Why Diagnostic |
|---|---|
| SRC01-E01 | The 57% workplace figure with 48K+ sample is maximally diagnostic — it definitively eliminates H2 and confirms workplace documentation |
| Absence of SE data | The targeted search failure for software engineering misrepresentation data is diagnostic for discriminating H1 (all contexts) from H3 (two contexts) |
Least Diagnostic Evidence¶
| Evidence ID | Why Non-Diagnostic |
|---|---|
| SRC04-E01 | The stable cheating rate finding is contextually important but does not discriminate between hypotheses about documentation quality |
Outcome¶
Hypothesis supported: H3 — Well-documented in academic and workplace contexts; software engineering data is sparse.
Hypotheses eliminated: H2 — Overwhelmingly contradicted by large-scale quantitative data.
Hypotheses inconclusive: H1 — Partially supported (workplace and academic confirmed) but overstates software engineering coverage.