Tarefa causcibench_real.q142
causcibench_realagentT2publicépoca 1, tentativa 1Execução: 20261015T171059Z-suite-causcibench_real-scripted-ded01dVeredito
-0.02152638843 vs -0.004Família do método (das chamadas de ferramentas): ols (regression)
Pergunta feita ao analista
How much does providing information about how masks protect the wearer increase the belief that masks protect others?
Estimativa (infer.ols): coef.T_mask_protectyou = -0,02153.
Valores esperados e de onde vêm
causcibench_real.gold.q142
- Fonte
- CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 142
- Tipo
- benchmark_gold
- Localização
- real_input.json, record id 142: fields effect and std_error
- Trecho
id 142 effect -0.004 std_error 0.012
- Valor como publicado
-0.004- Erro-padrão como publicado
0.012- Valor
- -0,004
-0.004(se 0,0120.012) - 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: -0,004
-0.004· 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 -0.02152638843 vs -0.004 | obrigatória | falhou | -0,004-0.004 | -0,0215264-0.021526388425171693 | relativa 0.05 | causcibench_real.gold.q142 | call_f310e08b |
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
| Argumento | Referência | Agente | |
|---|---|---|---|
| cluster | — | — | não fixado pela referência |
| controls | ["age","female","lucid_hhi","lucid_incmissing","ethnicity","education","workstatus","fluvaccine","partisanship_1","partisanship_2","partisanship_3","partisanship_5","partisanship_6","partisanship_7"] | ["age","female","lucid_hhi","lucid_incmissing","ethnicity","education","workstatus","fluvaccine","partisanship_1","partisanship_2","partisanship_3","partisanship_5","partisanship_6","partisanship_7"] | igual |
| dataset | "bokemper_experimental_evidence" | "bokemper_experimental_evidence" | igual |
| fe | — | [] | não fixado pela referência |
| intercept | — | true | não fixado pela referência |
| level | — | 0,950.95 | não fixado pela referência |
| se | — | "HC1" | não fixado pela referência |
| subset | — | — | não fixado pela referência |
| weights | — | — | não fixado pela referência |
| x | ["T_mask_protectyou"] | ["T_mask_protectyou"] | igual |
| y | "mskblfs_prtctothers" | "mskblfs_prtctothers" | igual |
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 0Início
How much does providing information about how masks protect the wearer increase the belief that masks protect others?
bokemper_experimental_evidence(agent)The randomized experiment examines the causal relationship between beliefs, social norms, and reported intentions to engage in mask promoting behavior. Subjects are randomly assigned to a control group, a ""Mask Protects You"" group, and a ""Mask Protects Others"" group. The control group reads a message unrelated to the experiment, the ""Mask Protects You"" group reads a message about how masks protect the wearer, and the ""Mask Protects Others"" group reads a message about how masks protect others. After the treatment, subjects are asked about their beliefs about masks. They then read three vignette scenarios, taking place at an ATM, in a park or in a meeting in their neighborhood. In the first scenario, they are asked about what they would do when they have forgotten to bring their mask. In the second scenario, they are asked about what they would do if someone else was not wearing their mask properly. In the third scenario, they are asked about their evaluation of someone who took action against someone not wearing their mask properly. Variables in the dataset: age: age of the subject female: whether the subject is female lucid_hhi: household income lucid_incmissing: whether the subject's income is missing ethnicity: ethnicity of the subject education: education level of the subject workstatus: work status of the subject fluvaccine: whether the subject has received the flu vaccine pool_self_scenario_atm: whether the first vignette scenario takes place at an ATM pool_self_scenario_meet: whether the first vignette scenario takes place in a meeting in the neighborhood pool_other_scenario_atm: whether the second vignette scenario takes place at an ATM pool_other_scenario_meet: whether the second vignette scenario takes place in a meeting in the neighborhood partisanship_1: whether the subject is a strong democrat partisanship_2: whether the subject is a democrat partisanship_3: whether the subject is a democrat lean partisanship_4: whether the subject is an independent partisanship_5: whether the subject is a republican lean partisanship_6: whether the subject is a republican partisanship_7: whether the subject is a strong republican T_mask_protectyou: whether the subject was in the ""Mask Protects You"" group T_mask_protectother: whether the subject was in the ""Mask Protects Others"" group mskblfs_prtctothers: whether the subject believes that masks protect others after the intervention mskblfs_prtctyou: whether the subject believes that masks protect the wearer after the intervention pool_self: own mask wearing behavior in the vignette scenario, 1 means they would continue the activity without a mask, 2 means they would continue the activity without a mask while keeping distance from others, 3 means they would abandon the activity, 4 means they would go retrieve their mask and then continue the activity pool_other: behavior when someone else is not wearing their mask properly, 1 means they would continue the activity as normal, 2 means they would continue the activity as normal while keeping distance from others, 3 means they would abandon the activity, 4 means they would ask the other person to wear the mask properly pool_self_others: whether others are wearing masks in the first vignette scenario pool_other_others: whether others are wearing masks in the second vignette scenario
filee94fe873cb4358fa045bc38ef25d976d69a12ed882c571b03327e0a5b7be7723· frame — - 1Turno do modelo
call_229aec70referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheTexto visívelcauscibench_metadata_rule/1: ols
Chamadas pedidasinfer.ols{ "controls": [ "age", "female", "lucid_hhi", "lucid_incmissing", "ethnicity", "education", "workstatus", "fluvaccine", "partisanship_1", "partisanship_2", "partisanship_3", "partisanship_5", "partisanship_6", "partisanship_7" ], "dataset": "bokemper_experimental_evidence", "x": [ "T_mask_protectyou" ], "y": "mskblfs_prtctothers" }
- 2Ferramenta
infer.olsv0.1.0call_f310e08bdecidido pelo modeloreexecutávelMotivo: causcibench_metadata_rule/1: ols
Argumentos
{ "cluster": null, "controls": [ "age", "female", "lucid_hhi", "lucid_incmissing", "ethnicity", "education", "workstatus", "fluvaccine", "partisanship_1", "partisanship_2", "partisanship_3", "partisanship_5", "partisanship_6", "partisanship_7" ], "dataset": "bokemper_experimental_evidence", "fe": [], "intercept": true, "level": 0.95, "se": "HC1", "subset": null, "weights": null, "x": [ "T_mask_protectyou" ], "y": "mskblfs_prtctothers" }Figuras call_f310e08b Chave Valor Valor bruto Erro-padrão IC coef.T_mask_protectyou Coefficient on T_mask_protectyou in the regression of mskblfs_prtctothers-0,0215264 -0.0215263884251716930,0102933 0.010293304210006597-0,0417093 – -0,00134343 Diagnósticosn_obs= 2.9052905k= 1616r2= 0,06675820.06675822551534394df_resid= 2.8892889se_type="HC1"n_clusters= —
Tabelas (1)
{ "coefficients": [ { "ci_high": 0.7045142692069545, "ci_low": 0.618293668455527, "coef": 0.6614039688312408, "p": 3.402903723407694e-173, "se": 0.021986240315504186, "t": 30.08263165234458, "term": "Intercept" }, { "ci_high": -0.0013434271737628768, "ci_low": -0.04170934967658051, "coef": -0.021526388425171693, "p": 0.0365883077524285, "se": 0.010293304210006597, "t": -2.0913001292864632, "term": "T_mask_protectyou" }, { "ci_high": 0.0022943399709127107, "ci_low": 0.0011894326161677716, "coef": 0.0017418862935402412, "p": 7.204975462668079e-10, "se": 0.00028175121045405225, "t": 6.182356025136959, "term": "age" }, { "ci_high": 0.01388247800151864, "ci_low": -0.02479083629045128, "coef": -0.005454179144466322, "p": 0.5802600341858667, "se": 0.009861689369013073, "t": -0.553067424898231, "term": "female" }, { "ci_high": 0.004429053157630859, "ci_low": 0.0013420605720303016, "coef": 0.0028855568648305805, "p": 0.0002511268612086388, "se": 0.0007871826483193436, "t": 3.6656764106669817, "term": "lucid_hhi" }, { "ci_high": -0.011111177852467423, "ci_low": -0.11695059834246768, "coef": -0.06403088809746756, "p": 0.017734357690731735, "se": 0.026989036418982693, "t": -2.372477738865532, "term": "lucid_incmissing" }, { "ci_high": 0.010769289483720534, "ci_low": -0.011907189429155075, "coef": -0.0005689499727172704, "p": 0.9216280783442844, "se": 0.0057824987362975625, "t": -0.09839171587637208, "term": "ethnicity" }, { "ci_high": 0.016980568629533472, "ci_low": 0.0022372636704911667, "coef": 0.009608916150012319, "p": 0.010642871901174442, "se": 0.0037595405628033964, "t": 2.5558751101350503, "term": "education" }, { "ci_high": 0.0013543182881567521, "ci_low": -0.014322478045480976, "coef": -0.006484079878662112, "p": 0.10491225307253212, "se": 0.003997580723918436, "t": -1.622000986713385, "term": "workstatus" }, { "ci_high": 0.013629007525164622, "ci_low": 0.0044929106440744805, "coef": 0.009060959084619551, "p": 0.00010279159986115439, "se": 0.002329703340301192, "t": 3.889318836383743, "term": "fluvaccine" }, { "ci_high": 0.08821932633786436, "ci_low": 0.024941865967129835, "coef": 0.056580596152497095, "p": 0.00046089202178993316, "se": 0.016135742944736183, "t": 3.5065380222207163, "term": "partisanship_1" }, { "ci_high": 0.07504304474992116, "ci_low": 0.0025940393604347717, "coef": 0.038818542055177964, "p": 0.03571005382133506, "se": 0.018474485554847323, "t": 2.101197456347724, "term": "partisanship_2" }, { "ci_high": 0.1136265839731692, "ci_low": 0.03967318662517237, "coef": 0.07664988529917079, "p": 0.00004940684102132136, "se": 0.018858105279602893, "t": 4.0645591994905255, "term": "partisanship_3" }, { "ci_high": 0.009612617022753955, "ci_low": -0.08502679139596751, "coef": -0.03770708718660678, "p": 0.11828738107499863, "se": 0.024133035013406703, "t": -1.5624676782534497, "term": "partisanship_5" }, { "ci_high": 0.005725932566785816, "ci_low": -0.07181960748282241, "coef": -0.0330468374580183, "p": 0.0947868942087313, "se": 0.019774101132066322, "t": -1.6712181877348893, "term": "partisanship_6" }, { "ci_high": -0.005428622144559921, "ci_low": -0.07563472877314578, "coef": -0.04053167545885285, "p": 0.0236468957499923, "se": 0.017902546705770246, "t": -2.2640173001637196, "term": "partisanship_7" } ] }- 195 rows were dropped because one of 'mskblfs_prtctothers', 'T_mask_protectyou', 'age', 'female', 'lucid_hhi', 'lucid_incmissing', 'ethnicity', 'education', 'workstatus', 'fluvaccine', 'partisanship_1', 'partisanship_2', 'partisanship_3', 'partisanship_5', 'partisanship_6', 'partisanship_7' was missing
- Digests dos dados
bokemper_experimental_evidencee2a4c2f6a5449108e627246309dda3cbfe09d0576c9c98eb7dc615e7e153447d- sha256 do resultado
b6eafa63e949c57bb407ce2a13ddbc708fa2888b24b9b8e149ca9e1c124a70da
- 3Turno do modelo
call_e37a8cd8referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.ols): coef.T_mask_protectyou = -0,02153.", "caveats": [], "figure_refs": [ "call_f310e08b:coef.T_mask_protectyou" ] }
- 4Guardaresposta aceita
- Números encontrados
- -0,02153
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 5Resposta
Estimativa (infer.ols): coef.T_mask_protectyou = -0,02153.
Figuras citadas:call_f310e08b:coef.T_mask_protectyou· Parada:answeredFigura da resposta:call_f310e08b:coef.T_mask_protectyou= -0,0215264-0.021526388425171693(se 0,01029330.010293304210006597) - 6Pontuação#0resposta com lastropassouobrigatória
every number backed - 7Pontuação#1figura da resposta próximafalhouobrigatória
-0.02152638843 vs -0.004
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
f0c42857d347c691c0ccbfb2cf70cc07defdb58ce8f32b48d6dc10832ecae68dO 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.
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| sources/causcibench_real.gold.q142.json | 1,2 KB | ca9c1944aaed79fc7e33755faced51fc348bc283e5a2bdf81e4de309e1e6ceb0 |
| specs/causcibench_real.q142.json | 8,5 KB | 3d5368ebad7babb80530332dcb0e282c7748bc6886b5c8d57b6c71dcba88c9fc |
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