Tarefa causcibench_real.q084

Veredito

Pergunta feita ao analista

Does labeling COVID-19 as the 'Chinese Virus' causally affect the level of blame the public assigns to the federal government?

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

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

Trajetória

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

1. 0Início

   Does labeling COVID-19 as the 'Chinese Virus' causally affect the level of blame the public assigns to the federal government?

   xu_does_mislabelling (agent)

   This dataset comes from an online survey experiment conducted in the United States on Amazon Mechanical Turk (MTurk) in March 2020, during the early stages of the COVID-19 pandemic. A total of 1,200 adult participants were included after excluding incomplete responses and failed manipulation checks. Respondents were randomly assigned to one of two groups: a control group where the virus was labeled COVID-19 and a treatment group where it was labeled the ""Chinese Virus"". The goal of the experiment was to test whether these labels influenced attitudes toward Chinese immigrants and shifted blame toward or away from the federal government. To capture sensitive views, the design combined a list experiment with direct survey questions. Dataset Variables: duration: the time subjects spent in survey cblame_1: blame in COVID19 group tblame_1: blame in ""Chinese Virus"" group mc: answer of the manipulation check question lc_c: list item count in 3-item COVID19 group direct_c: answer of the direct question in the 3-item COVID19 group lt_c: list item count in 4-item COVID19 group lc_t: list item count in 3-item ""Chinese Virus"" group direct_t: answer of the direct question in the 3-item ""Chinese Virus"" group lt_t: list item count in 4-item ""Chinese Virus"" group sex: 1=male; 0=female age: subjects' age race: subjects' race income: subjects' income worry: how worried subjects are about COVID-19 exposure twitter: frequency of using Twitter/Facebook party: party affiliation lean: party lean ideology: political ideology state: subjects' location state ch: 1=""Chinese virus"" group, 0=COVID19 group covid: item number in COVID19 group (1=4-item; 0=3-item) virus: item number in ""Chinese Virus"" group (1=4-item; 0=3-item) list: item subgroup (1=4-item group; 0=3-item group) blame: blame direct: answer of direct question ict.c: item count in COVID19 group ict.t: item count in ""Chinese Virus"" group ict: item count Y.c: social desirability measure in COVID19 group Y.t: social desirability measure in ""Chinese Virus"" group ido: numeric ideology measure ido1: simplified measure of ideology inc: numeric income measure wor: numeric measure of worry social: numeric measure of social media use ID: subject ID item: 3-item or 4-item ch1: a=COVID19; b=""Chinese Virus"" male: 1=male; 0=female female: 1=female; 0=male young: 1=age<=29 midage: 1=29<age<=49 old: 1=age>49 white: 1=white; 0=not white blk: 1=black; 0=not black his: 1=hispanic; 0=not hispanic asian: 1=asian; 0=not asian other: 1=other; 0=not other hwor: 1=worried or very worried; 0=not too worried or not worried at all lwor: 0=worried or very worried; 1=not too worried or not worried at all hsocial: 1=""social"">=2; 0=""social""<2 lsocial: 1=""social""<2; 0=""social"">=2 Dem: 1=Democrat; 0=other Rep: 1=Republican; 0=other Ind: 1=Independent; 0=other lib: 1=Liberal; 0=other con: 1=Conservative; 0=other mod: 1=Moderate; 0=other low: 1=Less than $25,000; 0=other med: 1=""inc"">=2 and <5; 0=other high: 1=""inc"">=5; 0=other mcr: 1=answer correct in manipulation check question; 0=answer wrong group: covid3=COVID19 3-item group; covid4=COVID19 4-item group; virus3=""Chinese Virus"" 3-item group; virus4=""Chinese Virus"" 4-item group

   file 7432cf2fb45b00f547e433f243fdbd8ecadf01b8141adbba4130824522108855 · frame —
2. 1Turno do modelo
   call_8b9b88eereferenceFim: 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": "xu_does_mislabelling",
       "x": [
         "ch"
       ],
       "y": "blame"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_68e1c275decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: ols

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "xu_does_mislabelling",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "ch"
     ],
     "y": "blame"
   }

   Figuras call_68e1c275

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.ch Coefficient on ch in the regression of blame	0,98687	0.9868697099037835	1,808621.8086183986590711	-2,56155 – 4,53528

   Diagnósticos

   • n_obs = 1.1991199
   • k = 22
   • r2 = 0,0002487430.0002487427166812939
   • df_resid = 1.1971197
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 51.81427253544662,
         "ci_low": 46.95111847952842,
         "coef": 49.38269550748752,
         "p": 1.0735948976427613e-221,
         "se": 1.2393688679866675,
         "t": 39.845034664868436,
         "term": "Intercept"
       },
       {
         "ci_high": 4.535284604064961,
         "ci_low": -2.561545184257393,
         "coef": 0.9868697099037835,
         "p": 0.58540932290685,
         "se": 1.8086183986590711,
         "t": 0.5456483858814326,
         "term": "ch"
       }
     ]
   }

   • 1 rows were dropped because one of 'blame', 'ch' was missing

   Digests dos dados

   xu_does_mislabelling 4ed38f68ca3d0323406ceb338cfe173ce776e8166aa30250c169c61662b6c20f

   sha256 do resultado

   7d9f1dc8179a977671118d0f54d218be8a50de89e8873e67342908be88456daf

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_ad8a8e4areferenceFim: 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.ch = 0,9869.",
       "caveats": [],
       "figure_refs": [
         "call_68e1c275:coef.ch"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   0,9869

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.ols): coef.ch = 0,9869.

   Figuras citadas: call_68e1c275:coef.ch · Parada: answered

   Figura da resposta: call_68e1c275:coef.ch = 0,986870.9868697099037835 (se 1,808621.8086183986590711)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória0.9868697099 vs 0.987

Épocas × verificações

Tentativas

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

• época 1, tentativa 1passoupontuada(exibindo)

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