questions

Documents about questions

• An empirical evaluation of probing questions investigating question comprehensibility in web surveys#177369SSOAR
• Chapter 2: The Forensic Mental Health Assessment: How to be a Mind Detective#275863OpenUCT
• Summary record of the 40th meeting : 5th Committee, held on Friday, 2 December 1988, New York, General Assembly, 43rd session.#462582UN Digital Library
• Table 1_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422552Figshare
• Table 3_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422553Figshare
• Table 5_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422555Figshare
• Table 4_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422556Figshare
• Table 8_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422557Figshare
• Table 7_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422558Figshare
• Table 6_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422559Figshare
• Table 9_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422561Figshare
• Table 2_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422565Figshare
• Table 2_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422568Figshare
• Supplementary file 1_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422560Figshare
• Supplementary file 4_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422562Figshare
• Supplementary file 3_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422563Figshare
• Supplementary file 2_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422564Figshare
• Supplementary file 7_Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.docx#422566Figshare
• Six questions pour tirer les leçons de la catastrophe de Fukushima sur le plan des facteurs organisationnels et humains#448818HAL (France)
• {'@classid': 'main title', '@classname': 'main title', '@schemeid': 'dnet:dataCite_title', '@schemename': 'dnet:dataCite_title', '@inferred': False, '@provenanceaction': 'sysimport:crosswalk:repository', '@trust': '0.9', '$': 'Health inequalities intervention tool frequently asked questions'}#491128OpenAIRE
• Posthumous Works of the Author of A Vindication of the Rights of Woman#41856Project Gutenberg
• Toolprint and Cognitive Depth: A Comparative Analysis of Novice and Experienced Teachers' AI-Generated Challenging Questions within the IAT Framework#453229OSF
• FC 207/14 - Recommandation n° 7 du rapport du Corps commun d’inspection intitulé Examen des progrès accomplis par les entités des Nations Unies dans le renforcement de la fonction d’enquête (JIU/REP/2020/1) – Extrait du rapport de la 124e session du Comité des questions constitutionnelles et juridiques#189610FAO Open Knowledge
• Supplementary file 8_Cross-sectional comparative evaluation of five large language model–driven chatbots on an expert-curated 44-question set of parent-facing questions about pediatric vitamin D deficiency.docx#669810Figshare
• Supplementary file 5_Cross-sectional comparative evaluation of five large language model–driven chatbots on an expert-curated 44-question set of parent-facing questions about pediatric vitamin D deficiency.docx#669811Figshare
• Supplementary file 7_Cross-sectional comparative evaluation of five large language model–driven chatbots on an expert-curated 44-question set of parent-facing questions about pediatric vitamin D deficiency.docx#669812Figshare
• Supplementary file 2_Cross-sectional comparative evaluation of five large language model–driven chatbots on an expert-curated 44-question set of parent-facing questions about pediatric vitamin D deficiency.docx#669813Figshare
• Supplementary file 3_Cross-sectional comparative evaluation of five large language model–driven chatbots on an expert-curated 44-question set of parent-facing questions about pediatric vitamin D deficiency.docx#669814Figshare
• Supplementary file 4_Cross-sectional comparative evaluation of five large language model–driven chatbots on an expert-curated 44-question set of parent-facing questions about pediatric vitamin D deficiency.docx#669815Figshare
• Supplementary file 6_Cross-sectional comparative evaluation of five large language model–driven chatbots on an expert-curated 44-question set of parent-facing questions about pediatric vitamin D deficiency.xlsx#669816Figshare
• Woman in the Nineteenth Century and Kindred Papers Relating to the Sphere, Condition and Duties, of Woman.#35192Project Gutenberg
• The End of the Middle Ages: Essays and Questions in History#55746Project Gutenberg
• Coordination questions : reports of the coordination bodies : annual overview report of the Administrative Committee on Coordination for 1993.#372681UN Digital Library
• Mompremier c. Ville de Montréal#681760CanLII
• Association du personnel administratif et professionnel de l'Université de Sherbrooke (APAPUS) c. Université de Sherbrooke#976608CanLII
• R. v. Penner#638549CanLII
• Logistik Unicorp Holdings Inc. c. Clearspring Capital Partners II#1053809CanLII
• JonesCo Contracting Incorporated v. Caldwell#638494CanLII
• Desjardins Assurances générales inc. c. Ladouceur#1039737CanLII
• Democracy Watch v. Canada (Attorney General)#638322CanLII
• Belousov c. Ville de Montréal#681765CanLII
• Fernbrook Homes (Bass Lake) Limited v. Cameron Stephens Mortgage Capital Ltd.#638384CanLII
• Commission des normes, de l'équité, de la santé et de la sécurité du travail c. Mantha#638402CanLII
• Saskatchewan Hockey Association Incorporated v Trayhorne#638454CanLII
• 9405-7890 Québec inc. c. DGP Towers inc.#681757CanLII
• 9256-0929 Québec inc. c. Ville de Montréal (Arrondissement Rivière-des-Prairies / Pointe-aux-Trembles)#654274CanLII
• Slepcsik v. Canada (Citizenship and Immigration)#638346CanLII
• Luo v. Yang#666769CanLII
• Hancock v. Canada (Attorney General)#1001759CanLII
• R. v R.P.#638529CanLII