Tarefa causcibench_real.q175

Veredito

Pergunta feita ao analista

Does providing credible information improve compliance with Covid guidelines?

Como o agente resolveu e como foi validado

Resumo passo a passo do que o agente executou, por que o caminho está correto e como cada verificação conferiu o resultado.

1. 1Preparação da base de dados
   bowles_countering— The data aims to test whether trusted WhatsApp messages can counter COVID-19 misinformation in Zimbabwe. Partnering with Internews and Kubatana, the researchers crafted short weekly messages: Week 1 emphasized transmission and social distancing, Week 2 debunked fake cures. At the WhatsApp broadcast list level, timing was randomized: treatment groups received messages on Monday, control groups on Saturday. To measure effects, a survey was sent on Thursday. Knowledge was assessed through factual questions, and behavior through a list experiment, where respondents received either a short list (only non-sensitive activities) or a long list (the same activities plus a sensitive one: visiting others during lockdown). Broadcast lists were blocked in groups of four, with random assignment across timing (treatment vs. control) and list length (short vs. long). Data variables ResponseId: identifier for the responses from the individuals Behavior: number of activities the respondent participated in, coded from 0 to 5 Id: identifier for individuals participating List: binary variable, equals 1 if the WhatsApp broadcast list is assigned to the long experimental list qualtrics: binary variable indicating response mode (1 if Qualtrics, 0 if WhatsApp) T: treatment indicator, equals 1 if the WhatsApp broadcast list is assigned to the treatment group female: binary variable, equals 1 if the respondent is female c_Q4: survey question on factual knowledge about COVID-19 transmission or prevention c_Q5: survey question on factual knowledge about misinformation or fake cures Knowledge: z-score of the respondent's answers to the factual questions c_Q4_1: sub-item of Q4 (first option, coded 1 if correct, 0 otherwise) c_Q4_3: sub-item of Q4 (third option, coded 1 if correct, 0 otherwise) c_Q4_4: sub-item of Q4 (fourth option, coded 1 if correct, 0 otherwise) c_Q4_5: sub-item of Q4 (fifth option, coded 1 if correct, 0 otherwise) block: randomization block (grouping of broadcast lists created at similar times) length_subscribe: number of months the WhatsApp broadcast list had been active Week: indicator for intervention week (1 = distancing message, 2 = misinformation/cures message) response_rate: fraction of weekly responses coming from WhatsApp per person Week_id: numeric identifier for the week urban: binary variable, equals 1 if the individual lived in an urban district
2. 2Conferência de lastro e resposta final

   Todos os 0 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   • ✓
     #0resposta com lastro

     every number in the answer was stated by a tool call
   • ✗
     #1figura da resposta próxima

     CauSciBench effect accuracy: |answer figure − gold| ≤ 5% of |gold|

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.

Nenhum caminho de referência registrado para esta tarefa.

Trajetória

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

1. 0Início

   Does providing credible information improve compliance with Covid guidelines?

   bowles_countering (agent)

   The data aims to test whether trusted WhatsApp messages can counter COVID-19 misinformation in Zimbabwe. Partnering with Internews and Kubatana, the researchers crafted short weekly messages: Week 1 emphasized transmission and social distancing, Week 2 debunked fake cures. At the WhatsApp broadcast list level, timing was randomized: treatment groups received messages on Monday, control groups on Saturday. To measure effects, a survey was sent on Thursday. Knowledge was assessed through factual questions, and behavior through a list experiment, where respondents received either a short list (only non-sensitive activities) or a long list (the same activities plus a sensitive one: visiting others during lockdown). Broadcast lists were blocked in groups of four, with random assignment across timing (treatment vs. control) and list length (short vs. long). Data variables ResponseId: identifier for the responses from the individuals Behavior: number of activities the respondent participated in, coded from 0 to 5 Id: identifier for individuals participating List: binary variable, equals 1 if the WhatsApp broadcast list is assigned to the long experimental list qualtrics: binary variable indicating response mode (1 if Qualtrics, 0 if WhatsApp) T: treatment indicator, equals 1 if the WhatsApp broadcast list is assigned to the treatment group female: binary variable, equals 1 if the respondent is female c_Q4: survey question on factual knowledge about COVID-19 transmission or prevention c_Q5: survey question on factual knowledge about misinformation or fake cures Knowledge: z-score of the respondent's answers to the factual questions c_Q4_1: sub-item of Q4 (first option, coded 1 if correct, 0 otherwise) c_Q4_3: sub-item of Q4 (third option, coded 1 if correct, 0 otherwise) c_Q4_4: sub-item of Q4 (fourth option, coded 1 if correct, 0 otherwise) c_Q4_5: sub-item of Q4 (fifth option, coded 1 if correct, 0 otherwise) block: randomization block (grouping of broadcast lists created at similar times) length_subscribe: number of months the WhatsApp broadcast list had been active Week: indicator for intervention week (1 = distancing message, 2 = misinformation/cures message) response_rate: fraction of weekly responses coming from WhatsApp per person Week_id: numeric identifier for the week urban: binary variable, equals 1 if the individual lived in an urban district

   file 3107fc57c0d67588eccecf4788cf3600ab114f22fa27694bebd9c1892a8e5049 · frame —
2. 1Turno do modelo
   call_b96ecbe5rules-1Fim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.",
       "caveats": [],
       "figure_refs": []
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Guarda
   resposta aceita

   Números encontrados

   —

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
4. 3Resposta

   Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.

   Figuras citadas: — · Parada: answered
5. 4Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
6. 5Pontuação
   #1figura da resposta próximafalhouobrigatóriano answer figure: the answer cites no effect of an estimator call

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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