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Althing (GPT-6 Luna)

2 runs · 1 dataset · 1 model

slug: althing-gpt-6-luna

0.809
Best SPS · globalopinionqa

Disaggregated subgroup scorecard. Each card below is one published run for this vendor; expand the question-type and demographic-subgroup sections to see the matrix beneath the headline SPS. Where coverage permits, 95% CI bands accompany the point estimate.

What each cell measures

Each cell compares this vendor's synthetic responses against real human respondents in that demographic subgroup — published survey ground truth, not a model's guess about the subgroup. Higher = closer to how that real subgroup actually answered.

Demographic conditioning here is not stereotyping: every conditioned score is checked against what real subgroup members said, not against assumptions about them — and where the vendor's output diverges from the real subgroup, the score drops.

  • globalopinionqa — ground truth: Durmus et al. 2023, Anthropic — llm_global_opinions

Low-n cells: cells with n < 30 are shown muted and tagged low n — suggestive only instead of color-graded; they are reported for transparency, not as findings.

CIs: "no CI — single run" marks point estimates from a single run with no confidence interval yet; treat the uncertainty as unknown, never zero.

Columns: Score = distributional parity (p_dist) restricted to the subgroup · p_cond = conditioning strength vs the unconditioned baseline · N = questions answered under that conditioning · Cov. = coverage dot derived from N (green = high, ≥100 · amber = medium, 50–99 · red = low, <50). Topic tables: SPS = Survey Parity Score for the topic · p_dist = 1 − mean(JSD) · p_rank = (1 + mean(τ)) / 2 · p_refuse = 1 − mean(|R_model − R_human|).

Multiple comparisons: with this many subgroup cells, a few extreme cells are expected by chance alone — read patterns across a dimension, not single cells.

globalopinionqa product

althing--gpt-6-luna--tdefault--tplstructured--74032abc

0.809 ± 0.027
SPS · 95% CI [0.782, 0.835] · n = 100
Question-type breakdown (6 topics)
Topic SPS p_dist p_rank p_refuse N
Health & Science low n — suggestive only 0.969 0.939 1.000 1.000 1
Economy & Work low n — suggestive only 0.935 0.900 0.969 0.949 3
General Attitudes low n — suggestive only 0.760 0.780 0.739 1.000 4
Trust & Wellbeing low n — suggestive only 0.751 0.721 0.781 1.000 2
International Relations & Security 0.719 0.751 0.688 0.987 60
Politics & Governance 0.696 0.717 0.676 0.963 30

Topics with N < 10 are muted and tagged "low n — suggestive only": too few questions for a stable 3-decimal score.

Not yet measured

This vendor has no demographic-conditioned runs for age, geography, education, or any other subgroup dimension on globalopinionqa. No cells are fabricated — scores appear here only when a conditioned run actually measured them.

How to submit a demographic-conditioned run →

globalopinionqa product

althing--gpt-6-luna--tdefault--tplstructured--c4cb9735

0.765 ± 0.033
SPS · 95% CI [0.732, 0.797] · n = 100
Question-type breakdown (6 topics)
Topic SPS p_dist p_rank p_refuse N
Health & Science low n — suggestive only 0.990 0.981 1.000 1.000 1
Economy & Work low n — suggestive only 0.947 0.895 1.000 0.949 3
General Attitudes low n — suggestive only 0.761 0.782 0.739 1.000 4
International Relations & Security 0.650 0.659 0.642 0.987 60
Trust & Wellbeing low n — suggestive only 0.644 0.571 0.717 1.000 2
Politics & Governance 0.618 0.597 0.639 0.963 30

Topics with N < 10 are muted and tagged "low n — suggestive only": too few questions for a stable 3-decimal score.

Not yet measured

This vendor has no demographic-conditioned runs for age, geography, education, or any other subgroup dimension on globalopinionqa. No cells are fabricated — scores appear here only when a conditioned run actually measured them.

How to submit a demographic-conditioned run →

No demographic conditioning data has been published for this vendor yet. The question-type matrix above shows topic-level parity; subgroup rows fill in once Althing-style conditioned runs land.

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