Interpreting metrics

You ran infereval evaluate, you ran infereval metrics, you got a wall of numbers. What do they mean?

The four numbers, in order

n (items)              : 4
coverage (M)           : 1.0000
coverage (per analyst) : 1.0000
κ_C(η, consensus)      : +1.0000
κ_F(η)                 : +1.0000
κ_F*(β) (inter-analyst): undefined

coverage — the participation gate

cov(η) = fraction of items where the model produced a substantive verdict (good or bad, not abstain).

This is your first sanity check. If it's low, nothing else in the report is fully informative — the kappa metrics are restricted to substantive items, so a 0.3 coverage means you're scoring M on 30% of the benchmark.

What low coverage usually means:

Coverage	Likely cause	What to do
0.95–1.00	Normal	Move on to kappa.
0.70–0.95	Some items are genuinely hard, or you have a small --max-tokens and a verbose model	Inspect the parse_status field in η: unparseable means the model talked but didn't say a verdict word. budget_clipped means the provider truncated by max_tokens — raise it. sample_failed means a provider error after retries.
0.30–0.70	Likely a verification prompt the model finds genuinely ambiguous, or a content-attribution disagreement (see Haiku-style perceptual default)	Inspect the prompts that produced abstains; try the paraphrase axis (experiments/paraphrase_axis_triangulation.py).
0.00–0.30	Often token-budget or model-misconfigured	Check parse_status distribution. If lots of budget_clipped, bump --max-tokens to 2048+. See providers.md for the reasoning-tokens issue.

Per-analyst coverage (cov_j(η)) is the analog for each human analyst — useful when authoring a benchmark to spot analysts who abstain a lot (their column has less inferential signal).

κ_C(η, consensus) — agreement with the analysts

Cohen's kappa against the analyst consensus c_i (strict-majority vote across analysts; abstain on tie or when most abstain). Restricted to items where both M and the consensus are substantive.

Reading scale, with caveats:

Value	Loose label	What it means
+1.00	perfect	M and the consensus agree on every substantive item.
+0.80 to +0.99	strong	M reliably tracks consensus; disagreements are isolated.
+0.40 to +0.80	moderate	M agrees better than chance but with notable exceptions. Decompose by tag to find the pattern.
+0.20 to +0.40	weak	M's verdicts are correlated with consensus but only modestly. Often this is a content-attribution disagreement, not an inferential one. Try the paraphrase axis (docs/concepts.md).
0.00 to +0.20	none	M is at or near chance.
< 0.00	anti-correlated	M is systematically disagreeing. Often this is a prompt-reading issue (the model is treating "GOOD" as something other than what the verification prompt says).
undefined	n/a	Either the substantive subset is empty (raise coverage) or the chance-expected agreement p_e = 1 (everyone — including M — is in a single class on this subset, so kappa has no signal).

The standard cautions about Landis–Koch labels apply: these are heuristic, not normative. In small-n benchmarks (n < 20), kappa is high-variance and the labels above are at best directional.

Switching the reference: by default the reference is the consensus, but you can compare M against any specific analyst:

infereval metrics eta.json --reference analyst:physician-a

This is informative when analysts disagree among themselves: comparing M to each analyst separately can show that M is closer to one analyst's column than the other. The paper's discussion of carving-relativity (§5) is exactly this kind of phenomenon — disagreement among competent labelers reflects different ways of carving the domain, and M may align with one carving more than another.

κ_F(η) — M as the (m+1)-th annotator

Fleiss' kappa treats M as if it were one more analyst on the panel. Per-item agreement is the pairwise-pair agreement count across all m+1 annotators, averaged across items, chance-corrected against the pooled marginal distribution.

κ_F is different from κ_C in two key ways:

1. Symmetric. κ_F doesn't single out a reference; every annotator contributes equally. This is appropriate when you don't want to privilege analyst consensus over M.
2. Pooled marginals. The chance-expected agreement is computed from the pooled distribution of all annotators' verdicts. This means κ_F = κ_C only by coincidence (typically only when the marginals are symmetric, e.g. under perfect agreement on a balanced item set). At m = 1, κ_F with M as the 2^nd annotator is not the same number as κ_C against that analyst, except in special cases.

Reading κ_F: same scale as κ_C. What you compare it to is the next metric.

κ_F*(β) — the inter-analyst baseline

This is the most important reference number when m ≥ 2. It's Fleiss' kappa computed over the analysts alone (no M).

The paper's Remark 4 makes the point: κ_F*(β) tells you how well the analysts agree with each other before M is in the picture. It's the ceiling above which M is doing something the analysts don't do. It's also the floor: if κ_F*(β) = 0.6, then M reaching κ_F = 0.5 is participating in the practice at a level not far from the analysts' own internal agreement — which is a strong result.

Comparison	What it tells you
κ_F(η) ≈ κ_F*(β)	M's inclusion doesn't lower inter-annotator agreement — it participates at human-analyst-level coherence.
κ_F(η) > κ_F*(β)	M is more consistent with the analyst consensus than the analysts are with each other. Plausible if M is just tracking the median, but worth investigating.
κ_F(η) << κ_F*(β)	M is systematically disagreeing with the analyst community. Decompose by tag.
κ_F*(β) = undefined	Either m < 2 (no inter-analyst comparison possible) or the analysts are unanimous on every item (no signal). Single-analyst benchmarks are perfectly fine — you just lose this comparison.

If your benchmark has m = 1, expect κ_F* to always be undefined and rely on κ_C for headline numbers.

Per-panel and cross-panel: independent-reference checks (v0.3.0+)

If your benchmark declares reference panels (per-analyst panel plus a benchmark-level primary_panel, see authoring_benchmarks.md Step 4b), infereval describe and infereval report surface two extra metrics:

κ_F*(β) per panel:
  primary (n_analysts=3) : +0.6824
  check   (n_analysts=2) : +0.7100

κ_C cross-panel (primary vs check) : +0.5800

Metric	What it is	Why it matters
inter_analyst_fleiss_per_panel	κ_F* computed within each declared panel	Tells you how internally coherent each panel is on its own. A primary panel with κ_F* = 0.10 and a check panel with κ_F* = 0.70 means the primary panel is the one to inspect for analyst-disagreement.
cross_panel_kappa	Cohen's κ_C between the two panels' per-item consensuses (default primary vs the first declared check panel; override with --check)	The construct-validity convergence check (R4 in closing_the_construct_validity_gap.md). A high cross-panel κ_C plus a high κ_C(η, primary-consensus) is the convergent half of a multi-trait/multi-method argument. A model that agrees with primary but disagrees with check is a warning sign that the primary consensus is panel-specific rather than carving-specific.

The same undefined rules apply as for the corresponding base metrics (empty substantive intersection, p_e = 1, single-analyst panel).

Decompositions: when the overall number isn't enough

A single κ_C = 0.40 could mean many different things:

• M is uniformly mediocre across all items;
• M is perfect on some items and chance on others;
• M is perfect on "easy" items (base inferences) and consistently wrong on "hard" ones (defeaters);
• M is fine on items with a particular tag and broken on others.

The first three are inferentially different and the fourth is a smoking gun for content-attribution issues. Decomposition is how you tell which it is.

By tag

infereval metrics eta.json --benchmark bench.json \
    --by-tag base-inference \
    --by-tag irrelevant-addition \
    --by-tag defeater

Produces a separate n / coverage / κ_C / κ_F / κ_F* block per tag. This is the single most useful tool the framework gives you for diagnosis. A pattern like:

By tag: base-inference        κ_C = +1.0000
By tag: irrelevant-addition   κ_C = -1.0000
By tag: defeater              κ_C = +1.0000

tells you a precise story: M handles base inferences and defeaters but is systematically wrong on irrelevant additions. This is exactly the cross-model finding from experiments/paraphrase_axis_triangulation.py: Claude Haiku 4.5 vs. the paper's analyst row.

Choose tag conventions when authoring the benchmark with this decomposition in mind. ["base-inference"], ["irrelevant-addition"], ["defeater"] are the obvious ones for RSR-style work; add domain-specific tags too (["nighttime"], ["coinfection"]).

By RSR target

infereval metrics eta.json --benchmark bench.json \
    --by-rsr-target '{"X": ["bd"], "A": ["ab"]}'

Filter to items probing one specific target inference. Useful when a benchmark covers multiple target inferences (multiple rsr_target groupings) and you want the per-target story.

Beyond metrics: structure, factor effects, sensitivity (v0.4.0+)

infereval metrics answers "how well does M agree with the analysts on this benchmark, this run". Three other CLI commands answer adjacent construct-validity questions; their outputs are also consumed by infereval report.

infereval structure benchmark.json — content-validity gate

Three deterministic checks on the benchmark itself (no model, no evaluation needed):

Check	What it verifies	A failure means
Containment	Every declared bearer appears in at least one item's Γ or Δ	An orphaned bearer — likely a carving slip; either delete it or add an item that exercises it.
RSR role consistency	For each rsr_target group, the role hierarchy (base → addition → defeater) is logically consistent	Misordered roles within a target — the RSR analysis won't be interpretable.
Base-case stability	Every rsr_target group contains at least one base-inference item (the Γ from which additions are built)	No anchor for the RSR sweep — additions and defeaters have nothing to attach to.

Read it as a gate: if structure returns non-zero, the benchmark fails content validity (R3) and metrics from it should be reported with that caveat. report echoes the structure verdict in §3 of its output.

infereval model eta.json --benchmark β.json — factor effects (v0.4.1)

Logistic regression of per-sample agreement (1 if M matches the reference, 0 otherwise) on the declared benchmark.factors levels, with item-clustered standard errors. Requires infereval[stats].

The output has two parts:

1. Per-factor joint Wald tests (factor_wald) — one p-value per declared factor, testing the null "this factor has no effect on agreement". A small p-value (e.g. < 0.05) means agreement varies systematically across the factor's levels — that factor is doing inferential work. A large p-value means the factor is not differentiating the model's behavior at this sample size.
2. Per-level coefficients (effects) — log-odds of agreement at this level relative to the alphabetically-first level of the same factor (the baseline). Positive coef → higher agreement than baseline; negative → lower. The p_value is the per-level Wald test; the conf_int_low/high is the 95% CI on the log-odds.

pseudo_r2 (McFadden) gives a rough sense of overall fit; values above ~0.2 indicate the declared factors meaningfully predict per-sample agreement. This is the GLMM-proxy specified in R10 of the construct-validity programme.

infereval sweep — sensitivity analysis (v0.4.2)

Re-runs metrics across a swept parameter (e.g. --vary tie_break --values abstain,good,bad) and reports a stability verdict based on the range of κ_C across the sweep:

κ_C range	Verdict	Reading
< 0.05	stable across the sweep range	Choice of parameter doesn't materially affect agreement. Safe to publish at any of the swept values.
0.05 ≤ r < 0.10	moderately sensitive	Some dependence on the parameter; report the specific value used and acknowledge the range.
≥ 0.10	varies substantively	Headline numbers are not robust to the parameter. Consider tighter parameter choices or a wider analyst panel before publishing a mastery claim.

The point is not to pick the best parameter post-hoc — that's p-hacking. The point is to show that the parameter you committed to (infereval evaluate ...) wasn't load-bearing. report consumes the sweep summary and downgrades the construct-validity verdict by one tier if the sweep is anything other than stable (R11).

infereval report — the construct-validity envelope (v0.5.0)

Consumes the claims file plus the outputs of the four analytical commands above and emits a single Markdown report with a deterministic verdict over five tiers (Strongly substantiated / Substantiated / Partially substantiated / Limited / Insufficient). The verdict is a function of the claims declared (R-numbers) plus the structural and inferential evidence; it is not a free-text summary.

Two interactions to be aware of:

• Suppression asymmetry: if negative_findings_suppressed: true is set in the claims file, the verdict downgrades one tier. This makes silence about negative findings explicitly costly in the headline number.
• Sweep instability penalty: any sweep verdict other than "stable" triggers a one-tier downgrade with a logged rationale.

The full mapping from claims and evidence to verdict tier is documented in closing_the_construct_validity_gap.md; the practitioner's walk-through is in construct_validity_workflow.md.

Edge cases the framework reports rather than hides

The metrics functions return None (with a logged warning) rather than raise. These cases are not bugs:

• κ_C undefined when substantive subset is empty. All items have M abstain, or the reference abstain, or both. No items to compare.
• κ_C undefined when p_e = 1. On the substantive subset, every item is the same class (all good or all bad) for both M and the reference. Chance and observed are both 1.0; their difference is 0/0. This often appears on small tag subsets where the analysts unanimously labeled one way and M matched.
• κ_F undefined when no items have all-substantive annotations. Same idea as κ_C empty S, applied across the full annotator set.
• κ_F* undefined when m < 2. Single-analyst benchmarks have no inter-analyst comparison.
• κ_F* undefined when analysts are unanimous. m ≥ 2 analysts agreeing perfectly on every item gives P̄_e = 1 and chance and observed are both 1.0. No signal.

Each of these returns null in the JSON output and "undefined" in the text / markdown output, with the reason logged at WARNING. They are diagnostic, not failures — they tell you something concrete about the structure of your benchmark or the model's behavior.

A worked reading: the live Haiku result from the M9 demo

We ran Claude Haiku 4.5 against the stop-sign benchmark with the original δ(ra) = "a is red" and got:

n (items)              : 4
coverage (M)           : 1.0000
κ_C(η, consensus)      : +0.2000
κ_F(η)                 : +0.0000
κ_F*(β) (inter-analyst): undefined        // m = 1

By tag: base-inference         κ_C undefined  (n=1; only one substantive item — p_e = 1)
By tag: irrelevant-addition    κ_F = -1.0000  (n=2; perfect anti-correlation!)
By tag: defeater               κ_C undefined  (n=1; same reason as base-inference)

Step-by-step reading:

1. Coverage is 1.00. No abstains; the model committed on every item. Good baseline.
2. Overall κ_C = +0.20. Above chance but well below agreement. Something is going wrong.
3. By-tag decomposition is the smoking gun. irrelevant-addition: κ_F = -1.00 means M perfectly disagrees with the analyst on every item tagged irrelevant-addition. Those are exactly the two items where Γ = {sa, n} and Γ = {sa, n, nr} — the nighttime / non-reflective additions.
4. Hypothesis: M reads is red as a perceptual claim (nighttime defeats visibility), not an intrinsic claim. Both readings are coherent; they just denote different things.

Following up on the hypothesis is what experiments/paraphrase_axis_triangulation.py does: hold the analyst row constant, vary δ(ra), see whether the disagreement comes from the bearer carving. (It does. The intrinsic phrasing a has the standard color of stop signs flipped row-1 to good and raised κ_C to +0.50.)

This is the methodology working: it gave a single number (+0.20), then a decomposition that localized the disagreement (irrelevant-addition: -1.00), then a hypothesis the analyst could test by varying δ. Each step in this chain is a concrete output of the framework.

When the numbers are surprising

If the kappa numbers don't match your prior intuition for the model, the order of diagnostic steps is:

1. Coverage low? Token-budget or prompt-ambiguity issue. Fix --max-tokens; inspect a few unparseable samples in η.
2. Decompose by tag. Is the disagreement concentrated in one tag? That's almost always content-attributional.
3. Inspect the actual prompts. infereval evaluate --dry-run prints what the model is seeing. Verify the framing is what you intended.
4. Probe with reasoning enabled. Outside the framework, ask the model the same question with room to explain. Often the model's reasoning makes its content-attribution explicit (Claude's verbatim "we cannot infer that the sign displays its standard color — only that it possesses that color as a property" is the canonical example).
5. Vary δ. If the disagreement is content-attributional, swapping the bearer phrasing for the analyst's reading should recover agreement. Run a second benchmark with the variant; see experiments/paraphrase_axis_triangulation.py for the pattern.

Where to go next

• concepts.md for the methodology's terms.
• authoring_benchmarks.md if your interpretation suggests you need a richer benchmark — especially the panel/factor/construction-metadata fields that feed the new analytical commands.
• construct_validity_workflow.md for the end-to-end practitioner's guide: how to chain structure → metrics → model → sweep → report into reproducible evidence for an inferential-mastery claim.
• closing_the_construct_validity_gap.md for which construct-validity requirements (R1–R21) each metric speaks to.
• providers.md for per-provider quirks (specifically, what to set --max-tokens to).
• experiments/paraphrase_axis_triangulation.py for a worked example of the diagnostic chain above.