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Tarefa causcibench_real.q037

causcibench_realagentT2publicépoca 1, tentativa 1Execução: 20261015T171059Z-suite-causcibench_real-scripted-ded01d

Veredito

passouPassou em todas as verificações obrigatórias.

Família do método (das chamadas de ferramentas): ols (regression)

Pergunta feita ao analista

Does exposure to government integrity information (corrupt, control, honest) causally increase trust in local government?

Resposta

Estimativa (infer.ols): coef.trustprime = 0,5405.

Valores esperados e de onde vêm

causcibench_real.gold.q037

Fonte
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 37
Tipo
benchmark_gold
Localização
real_input.json, record id 37: fields effect and std_error
Trecho
id 37 effect 0.541 std_error 0.045
Valor como publicado
0.541
Erro-padrão como publicado
0.045
Valor
0,5410.541 (se 0,0450.045)
Conferido por
arquivo do benchmark · Written by scripts/freeze_causcibench_real.py from the pinned file; equal to the `answer` and `std_error` of the same id in data/metadata_csv/real_input.csv at the pin.
sha256 da cópia lida
c1f4c8930675afff63d816d8ed066941ae92697e7bfc31e9d38519398dd80438 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://raw.githubusercontent.com/causalNLP/CauSciBench/d9bb43a2d90f261978cc5ac0b628c0484f6e8956/data/metadata_json/real_input.json
Licença
CauSciBench repository (MIT for code; metadata as published in the repository)
  • #1 figura da resposta próxima: 0,5410.541 · relativa 0.05

Verificações

Cada verificação, o que esperava, o que observou e a chamada que leu
#VerificaçãoObrigatóriaResultadoEsperadoObservadoTolerânciaFonteChamada
0resposta com lastro
every number backed
obrigatóriapassou—[]———
1figura da resposta próxima
0.5405212545 vs 0.541
obrigatóriapassou0,5410.5410,5405210.5405212545153025relativa 0.05causcibench_real.gold.q037call_c0178f71

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

caminho de referência do item · metadata_rule

Referência: infer.olsChamada do agente: infer.ols (call_c0178f71)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls[][]igual
dataset"liu_public_trust2""liu_public_trust2"igual
fe—[]não fixado pela referência
intercept—truenão fixado pela referência
level—0,950.95não fixado pela referência
se—"HC1"não fixado pela referência
subset——não fixado pela referência
weights——não fixado pela referência
x["trustprime"]["trustprime"]igual
y"trust""trust"igual

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

  1. 0Início

    Does exposure to government integrity information (corrupt, control, honest) causally increase trust in local government?

    liu_public_trust2 (agent)

    The dataset comes from a pre-registered vignette experiment examining the effect of trust in government on perceived collaborative governance in a hypothetical environmental program. Respondents were randomly assigned to three experimental conditions: Corrupt, Control, and Honest, about government integrity in a fictional U.S. city, Midtown. The information provided to the corrupt group explicitly states that the (hypothetical) municipality has corrupt officials. Similarly, for the honest group, the information states that officials are not corrupt. The control group is provided with no information. After reading the information about government integrity, subjects reported trust in government, perceptions of collaborative governance, and willingness to coproduce public services. Variables: trust: Trust in government (0-100; 0=never, 100=always) PPP: Support for public-private partnerships (0-100; 0=never, 100=always) PCP: Support for public-citizen partnerships (0-100; 0=never, 100=always) coproduce: Willingness to coproduce public services (0-100; 0=never, 100=always) placebo1: Placebo outcome variable for robustness checks (0-100; 0=never, 100=always) placebo2: Placebo outcome variable for robustness checks (0-100; 0=never, 100=always) duration: Time spent on the survey sex: 0=male; 1=female white: Race indicator (1=yes, 0=no) black: Race indicator (1=yes, 0=no) hispanic: Race indicator (1=yes, 0=no) asian: Race indicator (1=yes, 0=no) other: Race indicator (1=yes, 0=no) age: Age in years (18-100) income: 1=<25k; 2=25k-34,999; 3=35k-49,999; 4=50k-74,999; 5=75k-99,999; 6=100k-149,999; 7=150k+ education: 1=<high school; 2=high school/GED; 3=some college; 4=2-year degree; 5=4-year degree; 6=master's; 7=doctoral; 8=professional (JD, MD) ideology: 1=very liberal; 2=liberal; 3=moderate; 4=conservative; 5=very conservative gw: Belief that global warming is happening (-1=no, 0=don't know, 1=yes) gwh: Belief about human causation of global warming (-1=neither, 0=mostly natural, 1=mostly human) covid: Concern about COVID-19 (1=not at all, ..., 5=extremely concerned) covid_job: Concern about job loss due to the pandemic (1=strongly disagree, ..., 5=strongly agree) rand: The randomly assigned government integrity group: Corrupt, Control, and Honest trustprime: Encoding of government integrity group to which participants are assigned (0=Corrupt, 1=Control, 2=Honest) at: Response to the question, ""What is 17 + 63 equal to?"" The question was meant to test the attentiveness of the participants atpass: 1 = participant provided correct answer to attention test question, 0 otherwise mc: Response to the manipulation check question: Have you seen the information below from any previous part of this survey? ""Local and state government have more corruption than we assumed"" mcpass: 1 = participant passed the manipulation check, 0 = otherwise mcatpass: 1 = passed both attention and manipulation test, 0 = otherwise climatebelief: Average of gw and gwh"

    file fc860c838198dcffd59dcd954f0a1f1743f18b6fdb1a88ee81b4490493d3c3cf · frame —
  2. 1Turno do modelo
    call_9282fa8creferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Texto visível

    causcibench_metadata_rule/1: ols

    Chamadas pedidas
    • infer.ols
      {
        "controls": [],
        "dataset": "liu_public_trust2",
        "x": [
          "trustprime"
        ],
        "y": "trust"
      }
  3. 2Ferramenta
    infer.olsv0.1.0call_c0178f71decidido pelo modeloreexecutável

    Motivo: causcibench_metadata_rule/1: ols

    Argumentos
    {
      "cluster": null,
      "controls": [],
      "dataset": "liu_public_trust2",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "trustprime"
      ],
      "y": "trust"
    }
    Figuras call_c0178f71
    ChaveValorValor brutoErro-padrãoIC
    coef.trustprime
    Coefficient on trustprime in the regression of trust
    0,540521 0.54052125451530250,04639560.046395561698355340,449505 – 0,631537
    Diagnósticos
    • n_obs = 1.3371337
    • k = 22
    • r2 = 0,09584360.0958435875458935
    • df_resid = 1.3351335
    • se_type = "HC1"
    • n_clusters = —
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": 4.00808732295918,
          "ci_low": 3.759766156996179,
          "coef": 3.88392673997768,
          "p": 0,
          "se": 0.06329096694484292,
          "t": 61.36620954712954,
          "term": "Intercept"
        },
        {
          "ci_high": 0.6315374020998727,
          "ci_low": 0.44950510693073226,
          "coef": 0.5405212545153025,
          "p": 6.082820714072249e-30,
          "se": 0.04639556169835534,
          "t": 11.650279352786956,
          "term": "trustprime"
        }
      ]
    }
    Digests dos dados
    liu_public_trust2 075a3e2ce885dad588f2e9e6bee224ee62b6c7eb389d59cb0f989acf9a8d97b6
    sha256 do resultado
    b2db062ee6d255cd14c1c3d05e3082f4c71e18f4a9059b68747dad49e8a43a43
  4. 3Turno do modelo
    call_059c6d07referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Chamadas pedidas
    • submit_answer (meta-ferramenta)
      {
        "answer": "Estimativa (infer.ols): coef.trustprime = 0,5405.",
        "caveats": [],
        "figure_refs": [
          "call_c0178f71:coef.trustprime"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    0,5405
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa (infer.ols): coef.trustprime = 0,5405.

    Figuras citadas: call_c0178f71:coef.trustprime · Parada: answered
    Figura da resposta: call_c0178f71:coef.trustprime = 0,5405210.5405212545153025 (se 0,04639560.04639556169835534)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta próximapassouobrigatória0.5405212545 vs 0.541

Épocas × verificações

Resultado de cada verificação em cada época pontuada
#01
Época 1passoupassou

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

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