Tarefa causcibench_real.q123

Veredito

Pergunta feita ao analista

Does the headline being aligned with a participant's party increase the likelihood that they support its removal?

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 the headline being aligned with a participant's party increase the likelihood that they support its removal?

   ruth_partisan_conflict (agent)

   Social media companies have come under increasing pressure to remove misinformation from their platforms, but partisan disagreements over what should be removed have stymied efforts to deal with misinformation in the United States. This study aims to understand how party alignment can contribute to censorship opinions through randomized controlled trial. To test whether partisan conflict over content moderation may arise from the preference gap and party promotion, an experiment is embedded in a national survey of U.S. respondents. This survey was commissioned by the Knight Foundation and fielded by Ipsos in the summer of 2021. The survey was implemented on the Ipsos KnowledgePanel, which is described by Ipsos as a representative random sample - id: Participant ID - accuracy_order: Order in which accuracy question appeared - social_media_post_removed: Have you ever experienced the following? - Had a social media post removed by the social media company. - social_media_post_flagged: Have you ever experienced the following? - Had a social media post flagged, reported, or tagged with a warning label. - age: Age in years - gender: Gender - education: Education -- categorical - household_income: Household Income - race: Race - weight: Survey weight - hispanic: Hispanic ethnicity indicator - party_id: Party identification - party_id_dem: Democrat party ID indicator - party_id_rep: Republican party ID indicator - headline_order: Whether participants saw a headline in the first or second round - remove: How do you think the social media company should handle this headline? - censorship: Imagine that the social media platform removed this headline. Would you use the word “censorship” to describe this action? - accuracy: To the best of your knowledge, how accurate is the claim in the above headline? - harm: Some social media platforms allow users to report content as harmful. If you have the option of anonymously reporting this content as harmful, would you click the ""report as harmful content"" button for the above headline? - headline: Headline that participants saw - headline_orientation: Whether a headline is pro-Democrat or pro-Republican - headline_pro_dem: Indicates whether a headline is favorable for Democrats or unfavorable for Republicans - headline_pro_rep: Indicates whether a headline is favorable for Republicans or unfavorable for Democrats - accuracy_binary: Indicates whether a headline was deemed more or less accurate - social_media_most_common_newsformat: Indicates whether ""From social media"" was chosen in response to ""In which format do you get most of your news?"" - political_interest: Political interest. Average of national politics, state politics, local politics, and international politics news consumption, each measured on a 4-point scale from 1 (Not at all closely) to 4 (Very closely) - aligned: Indicates whether party ID and headline orientation are aligned (i.e., Democrat party ID and pro-Democrat headline, or Republican party ID and pro-Republican headline)

   file 97f2e2f94f0bea8dd79810f7624094eb1ae26f0442154f22e5db878a16abfa2b · frame —
2. 1Turno do modelo
   call_6810dd95naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "ruth_partisan_conflict",
       "x": [
         "aligned"
       ],
       "y": "remove"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_9228992cdecidido pelo modeloreexecutável

   Motivo: Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "ruth_partisan_conflict",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "aligned"
     ],
     "y": "remove"
   }

   Figuras call_9228992c

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.aligned Coefficient on aligned in the regression of remove	-0,0619854	-0.06198541480404062	0,02116690.02116687686903927	-0,103495 – -0,0204761

   Diagnósticos

   • n_obs = 2.1902190
   • k = 22
   • r2 = 0,00390420.00390419845664125
   • df_resid = 2.1882188
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 0.6230813506341047,
         "ci_low": 0.5648748537454572,
         "coef": 0.593978102189781,
         "p": 2.5817080571196602e-263,
         "se": 0.014840654930403766,
         "t": 40.02371222666928,
         "term": "Intercept"
       },
       {
         "ci_high": -0.020476136460264777,
         "ci_low": -0.10349469314781647,
         "coef": -0.06198541480404062,
         "p": 0.0034421472401531066,
         "se": 0.02116687686903927,
         "t": -2.928415712320154,
         "term": "aligned"
       }
     ]
   }

   • 50 rows were dropped because one of 'remove', 'aligned' was missing

   Digests dos dados

   ruth_partisan_conflict b7761f8222289aca206c65f1cae91e4794bdc8f8f1ce43835b3fb16b695f4277

   sha256 do resultado

   b68c774d263ecfeef6bfc16c8e818713be4344a257719fff4e28eb76495018d3

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_4000d23anaive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa ingênua (infer.ols): coef.aligned = -0,06199.",
       "caveats": [],
       "figure_refs": [
         "call_9228992c:coef.aligned"
       ]
     }

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

   Números encontrados

   -0,06199

   Sem lastro

   —

   Não citados

   —

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

   Estimativa ingênua (infer.ols): coef.aligned = -0,06199.

   Figuras citadas: call_9228992c:coef.aligned · Parada: answered

   Figura da resposta: call_9228992c:coef.aligned = -0,0619854-0.06198541480404062 (se 0,02116690.02116687686903927)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória-0.0619854148 vs -0.057

Épocas × verificações

Tentativas

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

• época 1, tentativa 1falhoupontuada(exibindo)

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