Tarefa causcibench_real.q084
causcibench_realagentT2publicépoca 1, tentativa 1Execução: 20261015T171122Z-suite-causcibench_real-baseline-4f971bVeredito
no answer figure: the answer cites no effect of an estimator callPergunta feita ao analista
Does labeling COVID-19 as the 'Chinese Virus' causally affect the level of blame the public assigns to the federal government?
Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.
Valores esperados e de onde vêm
causcibench_real.gold.q084
- Fonte
- CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 84
- Tipo
- benchmark_gold
- Localização
- real_input.json, record id 84: fields effect and std_error
- Trecho
id 84 effect 0.987 std_error 1.808
- Valor como publicado
0.987- Erro-padrão como publicado
1.808- Valor
- 0,987
0.987(se 1,8081.808) - 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: 0,987
0.987· relativa 0.05
Verificações
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number backed | obrigatória | passou | — | [] | — | — | — |
| 1 | figura da resposta próxima no answer figure: the answer cites no effect of an estimator call | obrigatória | falhou | 0,9870.987 | — | relativa 0.05 | causcibench_real.gold.q084 | — |
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
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 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
file7432cf2fb45b00f547e433f243fdbd8ecadf01b8141adbba4130824522108855· frame — - 1Turno do modelo
call_f69a4cd9rules-1Fim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.", "caveats": [], "figure_refs": [] }
- 2Guardaresposta aceita
- Números encontrados
- —
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 3Resposta
Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.
Figuras citadas:—· Parada:answered - 4Pontuação#0resposta com lastropassouobrigatória
every number backed - 5Pontuação#1figura da resposta próximafalhouobrigatória
no answer figure: the answer cites no effect of an estimator call
Épocas × verificações
| # | 0 | 1 |
|---|---|---|
| Época 1 | passou | falhou |
Tentativas
Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.
- época 1, tentativa 1falhoupontuada(exibindo)
Downloads desta tarefa
c110f939135b8564be4a1388612d4ec417294cbf85c7e95ad5dda9a14178f984O hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.
| Arquivo | Tamanho | sha256 |
|---|---|---|
| attachments/3fd37b7dca6f37f4b42a3a8a4170db47d464da2845d476f0b3be2606a52303d0.json | 151 B | 3fd37b7dca6f37f4b42a3a8a4170db47d464da2845d476f0b3be2606a52303d0 |
| attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json | 2,4 KB | 433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420 |
| attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json | 64,0 KB | 52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b |
| attachments/c1f32b32f40f6b3f93158632162f9284b63c06da6fd563e1bfbd1c0ad260a18e.json | 5,2 KB | c1f32b32f40f6b3f93158632162f9284b63c06da6fd563e1bfbd1c0ad260a18e |
| native/causcibench_real/causcibench_real.q084.e1.a1.json | 1,5 KB | 8f5a96c4fe09eac4f8dd58f3bfbe4e79af87f2ae42c0eca072757492b9d16056 |
| sources/causcibench_real.gold.q084.json | 1,2 KB | 57ba0457d19e24785a170a87cc411e56f2282c83214685310e29f0f9a06fa29a |
| specs/causcibench_real.q084.json | 7,0 KB | f35ebe268993cca25df31c83adcaf6e736230c847fa443fa917f9dfc7614aef2 |
| traces/causcibench_real.q084.e1.a1.jsonl | 10,8 KB | 463307a780b4898a23b030daf4d4f9f86c7fd9a00bc000db09ab075c832c48d0 |
| trajectories/causcibench_real.q084.e1.a1.jsonl | 6,8 KB | e6e736d08a424851bdf2289fb7d3a5748e2bae5637a087ff23ea2bf2489d6054 |