URGENT Python Task - Computational Modelling in the Humanities and Social Sciences

Computational Modelling in the Humanities and Social Sciences The House of Commons SPARQL endpoint ([login to view URL]) provides access to structured data about Members of Parliament (MPs) and questions that have been asked in the House of Commons. Use this data together with data from Wikidata (where necessary) to answer the following questions, presenting your findings in the form of a report of up to 3000 words, based on data about questions asked by MPs between 1 January 2023 and 30 September 2023 inclusive: [login to view URL] what extent do Members of Parliament (MPs) tend to ask questions that directly reference their own constituency or a location in it? You should answer this question by identifying named entities that refer to places or identifiable geographical features (e.g. “Dartford Crossing”, “Reading Gaol”, etc.) in asked questions, and determining whether or not these are located in the MP’s constituency using data from Wikidata. You are free to choose any reasonable method in doing so – even if doing so will result in some false negatives – (e.g. relying on Wikidata property P131 “located in the administrative territorial entity”), however you should estimate how reliable you believe your chosen approach is. (N.B. it is not expected or required that your approach will result in 100% accuracy.) [login to view URL] applying LDA topic modeling and analyzing the results, what (if any) identifiable regional differences are there in the types of questions asked – e.g. do MPs representing, say, constituencies located in the North of England tend to ask more questions about certain topics than those in Southeast England? In answering this question, you should start by aggregating data into regions larger than an electoral district, such as those denoted by the property “region of England” [login to view URL] (For simplicity, you may treat Scotland and Northern Ireland as two separate regions without further subdivisions, or alternatively use any reasonable administrative subdivisions for these regions as you see fit). Discuss the assumptions and limitations of your approach and analysis. The following SPARQL query can be used as a starting point: SELECT * WHERE { ?question <[login to view URL]> ?qnum . ?person <[login to view URL]> ?question . ?question <[login to view URL]> ?text . ?question <[login to view URL]> ?date . FILTER (?date >= "2023-01-01+00:00"^^xsd:dateTime && ?date < "2023-10-01+00:00"^^xsd:dateTime) } This query returns the following data: ?question Entity representing a question ?person Entity representing the person who asked the question ?qnum Numerical identifier for this question ?text The text of the question ?date The date the question was askedYour report must: 1. Document how data was collected, and briefly state any relevant modules or toolkits that were used. If using SPARQL queries not contained in your code (other than the one provided), you must include these as separate files within your code submission. 2. Describe your implementation and the models constructed. 3. Summarize your results in appropriate ways, including use of suitable visualizations. 4. Critically evaluate the models. This must involve critical analysis of the adequacy of the modelling (e.g. what are the assumptions the models rely on, and do they all hold; are there factors or biases that might invalidate conclusions drawn?), but should also involve some comparison with external data and/or published social/political science research on relevant subjects. 5. Discuss what conclusions can be drawn from the model. In this assignment, please note that: • You are not expected to resolve issues of lack of recorded data (or incorrectly recorded data) in additional data sources such as Wikidata. However, you are expected to account for the potential effects of these in your analysis, and should attempt to quantify these where possible. • You are allowed to reuse existing code – this includes open source modules and toolkits, Stack Overflow responses, code described in online tutorials, etc., provided that whenever you do this, you clearly indicate what you have reused and exactly where it came from (including a URL for an online source). You should document smaller instances of reuse (e.g. short code examples from Stack Overflow) as comments in your code, and longer ones both in your code and as citations to the source in your report. Marks will be awarded for the original parts of your work (i.e. your extensions to and adaptations or developments of any reused material). • Copying any material (this includes code and text) in your submission from any source without clear acknowledgement is likely to constitute plagiarism, which can have serious consequences as described in the Teaching and Learning Handbook: [login to view URL] • Marks will not be awarded under the “Technical depth/clarity of implementation” sections for parts of your code that reimplement code available in widely used libraries or modules. • You may use any programming language suitable for the task; use of Python 3.x is recommended where possible. • In writing your report, you should assume that the reader is familiar with everything covered during the lectures for this module. Concepts and techniques important to your implementation which were not introduced during the module should be explained. • The word limit is exclusive of figures and references. You may include a maximum of 20 figures (recommended: around 5-10 figures). Please use a 12pt font, single-spacing, and A4 pages. Your report should begin with an introduction, and does not require a separate abstract. Submission (2 files) • A written report as a PDF file. • An archive (.zip / .[login to view URL]) containing the code for your implementation, including instructions for how to run it in a [login to view URL] file. If any data that your code relies on is over 10 Mb in size, include instructions in this file for how to obtain it and do not include it in the archive. Your code is not marked separately, but serves as supporting evidence for the criteria indicated in the marking scheme. Marks Assessed by Q1. Appropriateness, technical depth and clarity of implementation Results and evaluation Conclusions and interpretation of results 15 Report Q2. Appropriateness, technical depth and clarity of implementation Results and evaluation Conclusions and interpretation of results Presentation (including academic writing, structure, and referencing) [login to view URL]:TM%3D1705497811:C%3Dr:IP%3D223.227.61.41-:S%3DwSHuPGZlcScPSyyd0Xmjv9Q%3B+path%3D/%3B+domain%[login to view URL]%3B+expires%3DWed,+17-Jan-2024+16:23:31+GMT