Tarefa causcibench_real.q148

Veredito

Pergunta feita ao analista

What is the effect of a radical right party's representation in parliament on mainstream left parties' position change on cultural protectionism?

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

   What is the effect of a radical right party's representation in parliament on mainstream left parties' position change on cultural protectionism?

   abouchadi_causal_effect (agent)

   The dataset aims to analyze the effect of radical right parties on the policy positions of the mainstream parties. It includes information on political parties belonging to various European countries, including their positions on cultural protectionism and environmental protectionism. Each country has minimum thresholds for voter shares that the political parties need to meet to get into the parliament. Thresholds vary by country, so in this dataset, we used voter shares centered around the threshold. Variable description: iso2c: iso2 character country code edate: election date party: CMP-code mainstream party partyname: name of mainstream party parfam: party family of mainstream party (CMP-coding) thrs: electoral threshold thrs_l: electoral threshold (lagged) er.v.c: radical right party vote share (centered on electoral threshold) er.v.c_l: radical right party vote share (centered on electoral threshold, lagged) er.in: radical right parliamentary presence (binary indicator) er.in_l: radical right parliamentary presence (binary indicator, lagged) rile.logit: rile score (according to Lowe et al. 2011) per607: multiculturalism positive (CMP coding) per608: multiculturalism negative (CMP coding) multic.logit_fd: cultural protectionism score (Lowe version, first difference) per608_fd: per608 score (first difference) multic.ratio_fd: cultural protectionism score (Kim and Fording version, first difference) af.bipolar_fd: cultural protectionism score (Alonso and da Fonseca version, first difference) meguid.bipolar_fd: cultural protectionism score (Meguid 2008 version, first difference) env.logit_fd: environment protection score (Lowe version, first difference)

   file d1326fd1180f16563bc4c0b2168ed9c9342616cc1e3baae3f152ad8d95b1097b · frame —
2. 1Turno do modelo
   call_dd084d3creferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: rdd

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "er.v.c_l"
       ],
       "dataset": "abouchadi_causal_effect",
       "x": [
         "er.in_l"
       ],
       "y": "multic.logit_fd"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_43533369decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: rdd

   Argumentos

   {
     "cluster": null,
     "controls": [
       "er.v.c_l"
     ],
     "dataset": "abouchadi_causal_effect",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "er.in_l"
     ],
     "y": "multic.logit_fd"
   }

   Figuras call_43533369

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.er.in_l Coefficient on er.in_l in the regression of multic.logit_fd	0,441378	0.441378163555753	0,3266590.32665888645197083	-0,200865 – 1,08362

   Diagnósticos

   • n_obs = 391391
   • k = 33
   • r2 = 0,006866150.006866153170495881
   • df_resid = 388388
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 0.12430376922825646,
         "ci_low": -0.32817970707288496,
         "coef": -0.10193796892231426,
         "p": 0.37623829538642006,
         "se": 0.11507151080207859,
         "t": -0.8858662601349361,
         "term": "Intercept"
       },
       {
         "ci_high": 1.083621180424954,
         "ci_low": -0.20086485331344794,
         "coef": 0.441378163555753,
         "p": 0.1774219688620705,
         "se": 0.32665888645197083,
         "t": 1.3511898248041372,
         "term": "er.in_l"
       },
       {
         "ci_high": 0.018975993223425233,
         "ci_low": -0.08807817380675494,
         "coef": -0.03455109029166485,
         "p": 0.2051690293731245,
         "se": 0.027225048831666758,
         "t": -1.2690919493035702,
         "term": "er.v.c_l"
       }
     ]
   }

   • 35 rows were dropped because one of 'multic.logit_fd', 'er.in_l', 'er.v.c_l' was missing

   Digests dos dados

   abouchadi_causal_effect 96131b8922392c31e04a3d9941dec6ce8caaf03f92da1d46a02f9189a65ba22c

   sha256 do resultado

   39c0b27c514ffb3734d9aa0386ef402f150f2c3fa900b3586cdc12361a27e1df

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_909142c4referenceFim: 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.er.in_l = 0,4414.",
       "caveats": [],
       "figure_refs": [
         "call_43533369:coef.er.in_l"
       ]
     }

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

   Números encontrados

   0,4414

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.er.in_l = 0,4414.

   Figuras citadas: call_43533369:coef.er.in_l · Parada: answered

   Figura da resposta: call_43533369:coef.er.in_l = 0,4413780.441378163555753 (se 0,3266590.32665888645197083)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória0.4413781636 vs 2.996

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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