Social Science

By Saqfa Jadoon, Dr. Muhammad Rizwan, Sadaf Butt. 1943

: This study looks at how Pakistan can promote sustainable religious tourism by making better use of its Buddhist World Heritage Sites, especially Taxila and Takht-i-Bahi . To explore this, the research combines different methods such as literature review, case studies, surveys, and interviews with key stakeholders. The aim is to understand both the opportunities and the challenges in positioning Pakistan as part of the global Buddhist pilgrimage network. The findings show that there is strong international interest, particularly from countries with large Buddhist populations. However, several barriers hold back progress, including poor infrastructure, weak branding, and fragmented policies. By learning from successful global practices, the paper suggests that developing sustainable Buddhist religious tourism in Pakistan could not only boost the economy but also help preserve cultural heritage and strengthen international cultural ties.

Citations in the corpus

• Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data (2018, neutral): “Digital traces of activity have exposed human behavior to quantitative analysis (Lazer et al. 2009; McFarland, Lewis, and Goldberg 2016).”
• Tampering with Twitter’s Sample API (2018, unknown): “great importance not only to social researchers but also to decision makers [3] in the con-”
• Big data and human geography (2013, unknown): “…scientists, big data offers the potential for a new era of computational social science; a new paradigm of ‘data-driven science’ that challenges established epistemologies through its blending of abductive, inductive and deductive approaches (Batty et al., 2012; Lazer et al 2009; Miller, 2010).”
• Metaphors of Big Data (2014, unknown): “Scientific facts are, like data, regularly framed as givens that are valid outside of a particular context rather than simply being pieces of discourse that are ascribed to specific actors and embedded in a specific context (Latour & Woolgar, 1979).”
• DStress: Efficient Differentially Private Computations on Distributed Data (2017, unknown): “Not all of these algorithms have privacy constraints, but there are many that do; for instance, cloud reliability [64], criminal intelligence [43, 62], and social science [14, 28, 45] all involve analyzing graphs that can span multiple administrative domains.”
• Curated Flows: A Framework for Mapping Media Exposure in the Digital Age (2016, unknown): “The burst of data becoming available from individuals’ online experiences is an exciting opportunity for research across the social sciences (Lazer et al., 2009).”
• The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, unknown): “This availability of large-scale data is profoundly changing the world we live in and has led to the emergence of a new discipline called computational social science [45]; finance, economics, marketing, public health, medicine, biology, politics, urban science and journalism, to name a few, have al”
• The Pushshift Reddit Dataset (2020, unknown): “2007) and computational social science (Lazer et al. 2009), and developed new fields like”
• Truth from the machine: artificial intelligence and the materialization of identity (2021, unknown): “…includes not only the traditional sciences, but social inquiry as well, with a growing community of researchers claiming to practice a ‘ computational social science ’ that ‘ leverages the capacity to collect and analyse data with an unprecedented breadth and depth and scale ’ (Lazer et al. 2009).”
• Identifying Platform Effects in Social Media Data (2016, unknown): “…and more subtle than has been appreciated; it is not just a matter of non-embedded researchers having access to the data (Sav-age and Burrows 2007; Lazer et al. 2009; Huberman 2012; boyd and Crawford 2012), but also that even when researchers have access, without full knowledge of the platform…”
• A framework for interpreting social media interactions in the public sector (2013, unknown): “At the same time, government opens additional channels for the influx of large amounts of data about digital interactions, content, and expressed online sentiments that need to be analyzed and interpreted to understand to what extent they support government's mission (Lazer et al., 2009).”