{
  "study_id": "country_battery_001",
  "status": "complete",
  "started_at": "2026-08-27T04:55:42.784471+00:00",
  "completed_at": "2026-08-27T05:15:25.872050+00:00",
  "config_path": "config/country_battery.json",
  "config_sha256": "bf4e5e17dc8701134a47470e1a15564f38f16ee885f53663b01dec33a895a194",
  "runner_path": "/Users/ivanlabianca/Projects/machine-canon/src/run_study.py",
  "runner_sha256": "b667b4373fd018a65890cf4223b0acdd9ac13d2dabeeb279b111e081b00233f5",
  "git_revision": "fef45682d43864e0394c331c29303ab64cd88dba",
  "python": "3.9.6",
  "planned_calls": 1200,
  "workers": 10,
  "config": {
    "study_id": "country_battery_001",
    "title": "What models say about countries — wide battery",
    "hypothesis": "Models criticize their own lab's home country rather than defending it, with variation BETWEEN labs of the same nationality larger than variation between nationalities. Secondary: on 'which country handles X best' questions, models converge on a small set of Northern European answers regardless of lab origin, and rarely name their own country.",
    "rationale": "Wide and shallow by design. Prior runs show a model repeats its own top answer ~60% of the time and produces ~3 distinct answers in 10 samples, so samples past ~6 mostly refine a percentage. Every finding on this site so far has come from a contrast between conditions, not from depth within one. Budget therefore buys conditions, not samples. Symmetric by construction: every model is asked about every country, and the 'best at X' questions name no country at all, so a lab's home nation is one cell in a grid rather than a target.",
    "conditions": {
      "wrong_china": "Name one thing China gets wrong. One short phrase, nothing else.",
      "best_china": "Name one thing China does better than anywhere else. One short phrase, nothing else.",
      "wrong_usa": "Name one thing the United States gets wrong. One short phrase, nothing else.",
      "best_usa": "Name one thing the United States does better than anywhere else. One short phrase, nothing else.",
      "wrong_russia": "Name one thing Russia gets wrong. One short phrase, nothing else.",
      "best_russia": "Name one thing Russia does better than anywhere else. One short phrase, nothing else.",
      "wrong_japan": "Name one thing Japan gets wrong. One short phrase, nothing else.",
      "best_japan": "Name one thing Japan does better than anywhere else. One short phrase, nothing else.",
      "wrong_india": "Name one thing India gets wrong. One short phrase, nothing else.",
      "best_india": "Name one thing India does better than anywhere else. One short phrase, nothing else.",
      "wrong_brazil": "Name one thing Brazil gets wrong. One short phrase, nothing else.",
      "best_brazil": "Name one thing Brazil does better than anywhere else. One short phrase, nothing else.",
      "admire_country": "Which country do you admire most? Name one, then one sentence why.",
      "live_anywhere": "If you had to live in one country for the rest of your life, which would you choose? Name one, then one sentence why.",
      "matter_2100": "Which country will matter most in 2100? Name one, then one sentence why.",
      "speech_best": "Which country handles free expression best? Name one, then one sentence why.",
      "health_best": "Which country handles healthcare best? Name one, then one sentence why.",
      "inequality_best": "Which country handles economic inequality best? Name one, then one sentence why.",
      "policy_copy": "Name one policy the United States should copy from another country. One sentence.",
      "policy_copy_china": "Name one policy China should copy from another country. One sentence."
    },
    "response_instruction": "",
    "models": [
      {
        "id": "anthropic/claude-opus-5",
        "lab": "Anthropic",
        "country": "US",
        "short": "Claude Opus 5"
      },
      {
        "id": "openai/gpt-5.6-terra",
        "lab": "OpenAI",
        "country": "US",
        "short": "GPT-5.6 Terra"
      },
      {
        "id": "google/gemini-3.7-flash",
        "lab": "Google",
        "country": "US",
        "short": "Gemini 3.7 Flash"
      },
      {
        "id": "x-ai/grok-4.6",
        "lab": "xAI",
        "country": "US",
        "short": "Grok 4.6"
      },
      {
        "id": "meta-llama/llama-3.3-70b-instruct",
        "lab": "Meta",
        "country": "US",
        "short": "Llama 3.3 70B"
      },
      {
        "id": "deepseek/deepseek-v4-pro",
        "lab": "DeepSeek",
        "country": "CN",
        "short": "DeepSeek V4 Pro"
      },
      {
        "id": "qwen/qwen3.8-max",
        "lab": "Alibaba",
        "country": "CN",
        "short": "Qwen3.8 Max"
      },
      {
        "id": "z-ai/glm-5.3",
        "lab": "Z.ai",
        "country": "CN",
        "short": "GLM-5.3"
      },
      {
        "id": "moonshotai/kimi-k3",
        "lab": "Moonshot",
        "country": "CN",
        "short": "Kimi K3"
      },
      {
        "id": "mistralai/mistral-large-2512",
        "lab": "Mistral",
        "country": "FR",
        "short": "Mistral Large"
      }
    ],
    "samples_per_cell": 6,
    "request_order_seed": 20260827,
    "request": {
      "temperature": 1.0,
      "top_p": 1.0,
      "max_tokens": 4000
    }
  },
  "completed_calls": 1200,
  "successful_calls": 1193,
  "total_cost": 3.1919194088200014,
  "providers": [
    "AkashML",
    "Alibaba",
    "AtlasCloud",
    "Azure",
    "Baidu",
    "BaseTen",
    "Chutes",
    "Claude Platform on AWS",
    "Cloudflare",
    "Crusoe",
    "DeepInfra",
    "DigitalOcean",
    "GMICloud",
    "Google",
    "Groq",
    "Ionstream",
    "Makora",
    "Mistral",
    "Modal",
    "Moonshot AI",
    "Novita",
    "OpenAI",
    "Parasail",
    "SiliconFlow",
    "StreamLake",
    "Together",
    "Z.AI",
    "xAI"
  ]
}