Pular para o conteúdo

Tarefa crb_papers.q033

crb_papersagentT2publicépoca 1, tentativa 1Execução: 20261015T170957Z-suite-crb_papers-baseline-dae0c1

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, nenhuma estimativa citada.
no answer figure: the answer cites no effect of an estimator call

Pergunta feita ao analista

Did states that adopted direct primaries in 1912 experience an increase in the log of the state-average daily candidate newspaper advertisements (week before the election) from 1910 to 1912 relative to states that had not yet adopted?

Resposta

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

Valores esperados e de onde vêm

crb_papers.q033.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 33 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['33'].effect and .standard_error
Trecho
index 33: effect 0.61347, standard_error 0.223833
Valor como publicado
0.61347038637508
Erro-padrão como publicado
0.22383298763880474
Valor
0,613470.61347038637508 (se 0,2238330.22383298763880474)
Conferido por
arquivo do benchmark · Read from the catalog at the pinned revision by scripts/freeze_crb_papers.py; the catalog states the value its estimation script prints.
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/causal_queries.json
Licença
MIT
  • #2 figura da resposta próxima: 0,613470.61347038637508 · relativa 0.000001 · absoluta 2.2383298763880474e-7
  • #3 figura da resposta próxima: 0,2238330.22383298763880474 · relativa 0.000001 · absoluta 2.2383298763880474e-7

crb_papers.q033.script

Fonte
CausalReasoningBenchmark reference estimation script for index 33
Tipo
software
Localização
solutions/research_papers/DiD/Hirano_etal_2022/question_2/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 1742 bytes at the pinned revision; the reference path of the item is a transcription of this script into typed transforms and tool calls (never executed here).
Implementação
independente da ferramenta
sha256 da cópia lida
21726561292a6bc9d1ceffe81ded25dedb9065022434ba3e8605839c9231e6d5 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/DiD/Hirano_etal_2022/question_2/estimation.py
Licença
MIT

    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
    no answer figure: the answer cites no effect of an estimator call
    obrigatóriafalhou[0.1747577306030227,1.0521830421471372]—interval · [0.1747577306030227, 1.0521830421471372]——
    2figura da resposta próxima
    no answer figure: the answer cites no effect of an estimator call
    registradafalhou0,613470.61347038637508—relativa 0.000001 · absoluta 2.2383298763880474e-7crb_papers.q033.gold—
    3figura da resposta próxima
    no answer figure: the answer cites no effect of an estimator call
    registradafalhou0,2238330.22383298763880474—relativa 0.000001 · absoluta 2.2383298763880474e-7crb_papers.q033.gold—

    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 · benchmark_script

    Referência: infer.olsO agente não fez uma chamada de estimação que a resposta cite.

    Trajetória

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

    1. 0Início

      Did states that adopted direct primaries in 1912 experience an increase in the log of the state-average daily candidate newspaper advertisements (week before the election) from 1910 to 1912 relative to states that had not yet adopted?

      ds_0017 (agent)

      | Variable Name | Description | | --- | --- | | (index) | Row index from the CSV export; carries no substantive information. | | state | Two-letter U.S. state postal abbreviation identifying the state associated with the observation. | | year | Calendar year in which the state held the referenced general election. Most elections occurred in November of even-numbered years, though some states held major general elections in other months or in odd-numbered years; the value here reflects the actual calendar year of that election. | | ln_x_cand_all | Log-transformed average number of newspaper advertisements per day placed by or on behalf of individual candidates in the seven days preceding the general election; constructed as ln(ads per newspaper-day + 1). Source counts come from hand-coding full newspaper issues, then averaging ad counts to the newspaper-year and aggregating to a state-year mean. Included offices typically comprise statewide executive posts, state legislatures, U.S. House, judicial, and many local offices; advertisements focused solely on parties are tracked separately, and candidate ads for U.S. Senate and the presidency are excluded from this summary measure. | | prim_sw | Indicator for whether, in that year, the state used state-administered direct primary elections to nominate candidates for statewide offices (1 = direct primaries in use; 0 = nomination by caucus/convention or other non-primary procedures). Reflects the institutional status in each state-year during the late 19th and early 20th centuries. | Dataset Context: This state-year panel assembles historical evidence on campaign communications during an era when newspapers were the dominant mass medium. Researchers reviewed issues from 90 local newspapers across 33 mostly non-Southern states between 1880 and 1930, focusing on the week immediately preceding each state’s general election. Political advertisements were hand-coded at the ad level, distinguishing candidate-focused content from party-only notices. To make newspapers comparable despite different publication schedules and occasional missing issues, counts were normalized to advertisements per newspaper-day, averaged to the newspaper-year, and then aggregated to a state-year mean. The offices captured in the primary advertising measure are those commonly elected across states throughout the period: statewide executives (e.g., governor, attorney general), state legislatures, the U.S. House, judicial posts, and many local offices. Candidate advertising for U.S. Senate and for the presidency was excluded from that main summary to keep the measure focused on offices most consistently affected by state-level electoral rules of the time. The log transformation adds one before taking logs so that years with zero observed advertising are retained on a continuous scale. The panel spans a period of substantial institutional change often associated with Progressive Era reforms. States transitioned at different times to practices such as direct primary elections for nominating statewide candidates, and the dataset records whether a state employed direct primaries in each year. Because election timing varied across states—some held major contests in odd-numbered years or in months other than November—the year variable records the actual calendar year of the pertinent general election. Overall, the dataset provides a harmonized view of newspaper-based campaign activity across places and time, facilitating comparisons of advertising intensity during a formative phase in U.S. electoral development.

      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      ref_adopt (reference)
      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      3. filter {"query":"prim_sw == 1"}
      4. collapse {"aggs":[{"column":"year","fn":"min","name":"adopt_year"}],"by":["state"]}
      ref_panel (reference)
      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      3. filter {"query":"year in [1910, 1912]"}
      4. merge {"how":"left","on":["state"],"with":"ref_adopt"}
      5. merge {"how":"left","on":["state"],"with":"ref_years"}
      6. merge {"how":"inner","on":["state"],"with":"ref_pre"}
      7. merge {"how":"inner","on":["state"],"with":"ref_post"}
      8. filter {"query":"nyears == 2 and p_pre == 0 and ((adopt_year == 1912 and p_post == 1) or ((adopt_year != adopt_year or adopt_year > 1912) and p_post == 0))"}
      9. derive {"args":{"cmp":"eq","column":"p_post","value":1},"name":"Treated","op":"indicator"}
      10. derive {"args":{"cmp":"eq","column":"year","value":1912},"name":"Post","op":"indicator"}
      11. derive {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}
      ref_post (reference)
      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      3. filter {"query":"year == 1912"}
      4. select {"columns":["state","prim_sw"]}
      5. rename {"map":{"prim_sw":"p_post"}}
      ref_pre (reference)
      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      3. filter {"query":"year == 1910"}
      4. select {"columns":["state","prim_sw"]}
      5. rename {"map":{"prim_sw":"p_pre"}}
      ref_years (reference)
      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      3. filter {"query":"year in [1910, 1912]"}
      4. collapse {"aggs":[{"column":"ln_x_cand_all","fn":"count","name":"n"}],"by":["state","year"]}
      5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["state"]}
    2. 1Turno do modelo
      call_44a4a975rules-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": []
        }
    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
    7. 6Pontuação
      #2figura da resposta próximafalhouregistradano answer figure: the answer cites no effect of an estimator call
    8. 7Pontuação
      #3figura da resposta próximafalhouregistradano answer figure: the answer cites no effect of an estimator call

    Épocas × verificações

    Resultado de cada verificação em cada época pontuada
    #0123
    Época 1passoufalhoufalhoufalhou

    Tentativas

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

    Downloads desta tarefa

    Pacote da tarefa (.zip)
    86,2 KB · sha256 e5ba68163d7a151642756f2c1e25a8b70d52fa5ff612729684cb15f6be1ed0dc

    O 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.

    Downloads desta tarefa
    ArquivoTamanhosha256
    attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json2,4 KB433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420
    attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json64,0 KB52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b
    attachments/9eddeb28eb536480b57a1e8e0ec16d955ae0682f6ffe398116773f144230af73.json3,8 KB9eddeb28eb536480b57a1e8e0ec16d955ae0682f6ffe398116773f144230af73
    attachments/c44286fec11e6465363ba68790a7e5d19acc9d2f4aa44dd827753e3995ab6fb1.json259 Bc44286fec11e6465363ba68790a7e5d19acc9d2f4aa44dd827753e3995ab6fb1
    native/crb_papers/crb_papers.q033.e1.a1.json2,7 KBeb0d44354814e0a8aae0765a4d27b25bfc7bf9d4e790ae81bf31563953e9723f
    sources/crb_papers.q033.gold.json1,1 KBc18318e6c86607f72d7d19907011284a84343ad1e435300aa2d9f1aa45ee6aa7
    sources/crb_papers.q033.script.json1,1 KBdabfdba8eec45c6adbc84417b5c4b7d4c733bd915c661fa65e1a12f968719abb
    specs/crb_papers.q033.json14,1 KB03e7caead33562a0f4725baf0c7c0fbb33071ba2b629d1759c6a9221075c185a
    traces/crb_papers.q033.e1.a1.jsonl9,5 KB867f4c4c086f015e6f8ec5daac1fcb9c3d40209d0defa14ed33831f10384d7c3
    trajectories/crb_papers.q033.e1.a1.jsonl11,4 KB64c5e0fd77212ec6c0ab2b474b9dfd912161f7d1a4044be7031b438433835943