# Inclusive Data Research Skills for Arts and Humanities/Data inequalities and power
## What is power?

Contributor 6: Power is with policymakers at the government level and academic funders

## What is the challenge?
### Challenge Structure:

Defining challenges
What is the context?
Examples and sources
Challenge 1: Chinese queer culture study
Government Censorship: The Chinese government tightly controls information, particularly on topics deemed sensitive or politically controversial. Research on queer issues may be subject to censorship, with certain topics or findings deemed unacceptable or politically subversive. Chinese social media regulation of queer, THE GENERAL RULES, restrict all queer-related mediated posts in China, which means if you have queer-related posts, you will be banned.
Stakeholders: the Chinese government, including the regulatory authority responsible for media and information control.
Lack of Official Recognition: China does not officially recognize same-sex relationships, and LGBTQ+ rights are not fully protected under the law. This lack of recognition may limit the availability of data and resources for researchers and contribute to societal stigma surrounding queer issues.
Stakeholders: the Chinese government, particularly social media policymakers responsible for enacting laws
Social Stigma and Discrimination: Despite some progress in recent years, LGBTQ+ individuals in China still face significant social stigma and discrimination. This stigma can affect research participants' willingness to disclose their experiences or participate in studies, leading to challenges in data collection and interpretation.
Stakeholders: Chinese society, including individuals, communities, and institutions that perpetuate social stigma and discrimination against LGBTQ+ individuals.
Language and Cultural Barriers: For researchers from outside China, language and cultural barriers can pose additional challenges to conducting research on queer issues in the country.
Stakeholders: Researchers, academics, and institutions from diverse linguistic and cultural backgrounds seeking to research LGBTQ+ issues.
Challenge 2: Date Access Limitations- stem from diverse factors, including privacy regulations, legal and ethical considerations, proprietary constraints, and technical barriers.
- Privacy regulations often require strict controls on the access to datasets containing sensitive information, prompting researchers to navigate compliance intricacies. (Stakeholders: individuals; researchers and entities that responsible for overseeing and ensuring the privacy date and regulations)
- Proprietary datasets owned by private entities may have restricted access due to commercial interests, necessitating negotiations or collaborative efforts. (Stakeholders: private Entities, owners of the proprietary datasets in protecting their data)
- Some data sources may limit usage to specific purposes, such as academic research, and researchers must respect these conditions. (Data providers, researchers who determining their data usage)
- Accessing data through APIs may encounter rate limits, prompting researchers to manage requests responsibly. (Stakeholders; API Providers; IT Teams manage server loads and limit)
- Data quality, security, and confidentiality may also contribute to access restrictions. (Date subjects; data providers)
Challenge 3: When discussing the challenges that women face in the context of gender and data colonization, particularly in terms of data methods and skills, the challenges are:
* Data Collection Bias: There may be biases in the collection of data concerning women, resulting in incomplete or stereotyped information. This can affect the accurate analysis and understanding of women as a demographic.
* Insufficient Gender Sensitivity: Data scientists and analysts might lack gender sensitivity, leading to inadequate consideration and interpretation of gender factors. This oversight can result in the neglect or mishandling of gender-related issues in data analysis.
* Lack of Gender Diversity: Data science teams may lack gender diversity, leading to the oversight of women's needs and experiences in the development of data methods and skills.
* Gender Technology Gap: Women may face a technology gender gap, wherein they have fewer opportunities and skills to engage with digital technologies compared to men. This can impact their development in fields related to data science.
* Lack of Gender Data Expertise: Data scientists and analysts may lack specialized knowledge in gender data, making it challenging to interpret and analyze data related to women accurately.
* Insufficient Privacy Protection: Privacy protection for women in data collection and processing may be inadequate, potentially leading to the misuse and inappropriate use of their personal information.
* Limited Digital Literacy and Skills Training: The lack of digital literacy and skills training specific to women can restrict their participation in the digital society, affecting their development in fields related to data science and technology.
Stakeholders: government agencies, businesses and tech companies, social media platforms and general public(specifically for women)
Challenge 4:
* Issues of Informed Consent: Ensuring that the elderly fully understand and consent to data usage is a significant challenge during data collection. They might not fully grasp how their data is being collected and used, especially in the face of complex data collection and processing technologies. In addition, the majority of Chinese elderly influencers have teams running their accounts, so informed consent decisions for data use may involve the actual account holders.
* Technological Capability Gap: Elderly people generally are not as proficient with technology as younger individuals, which could create barriers in understanding and managing their own data. For instance, they may not know how to control or refuse access and usage of their data. This challenge can also link to the above one.
* Data Representativeness and Bias: The data collected may not adequately represent the diversity of the elderly population, potentially leading to biased decisions and analyses based on this data. For example, collecting data only from elderly individuals in a particular region or socio-economic class may not represent the entire elderly demographic.
* Stakeholder: Actual account holder, which mainly are the MCN agency.
Challenge 5: Digitalisation and downloadability hierarchy of resources and data:
* Easier to find and access the ‘big names’ English publications
* Less open-access/free resources about the Global South/Asia
* Moderation of data
* [E.g.1]The expertise and advice of supervisors on using certain reading lists
** The preferences for those set out by the more ‘prestigious’ institutions
*** Who sets the list?
*** Who decides which readings are "essential" and which are not?
* [E.g.2] When it comes to readings about art and politics, the "essential" readings are Western-centric. Most case studies and topics are about European and North American historical events and artistic works or those events/works that the Western powers were involved in (like the Vietnam War).
* Stakeholders:
** Students: whether they use more recognised sources can affect whether they can pass their exams/get their degrees?
** Researchers: complying the sources they used to the hierarchy can help them get published more easily?
** Academic advisors/supervisors (Authors/Makers of reading lists?): where they received their academic training determine what kind of materials they advise their tutees/students to refer to e.g. many Asian scholars were trained in the Western/Westernised institutions
** Publishers: publications related to the Western ‘big names’ are more popular
** Audience of research projects:
*** Global North/West: they do not have to expose themselves to non-Western thinking
*** Global South/East: they have to familiarise themselves with the thinking of the Western ‘big names’ in order to the conceptual/scholarly framework of the research projects
Challenge 6: Data tools
* Funding for access to data tools in humanities research because funders deem the humanities less requiring of these tools. E.g.: A supervisor asking questions whether or not student need NVIVO software in qualitative research in Arts.
* Tools are expensive and impossible for many researchers in global south to access. E.g: A person doing a research in Pakistan cannot afford tools that are priced in dollars.
* Tools are not global-friendly i,e. to interpret data in different languages - For example: AI transcription generators only transcribe in English and its hard to transcribe long interviews in Urdu language etc.
* E.g. Brandwatch visualises data gathered mainly from English media. Researchers have to gather and visualise non-English data by themselves. There is extra workload for researchers handling non-English data. In other words, research projects working on or using non-English data can subject to more time/resource restrictions.

#### Who are the stakeholders?

Funding bodies
Licensing softwares
Designer of softwares - incorporating languages
Institutions and their postgraduate and research bodies that allocate budget for research

## Tackling the challenges
### Collective Bibliography of theoretical frameworks

- Acilar, A. and Sæbø, Ø., 2023. Towards understanding the gender digital divide: A systematic literature review. Global knowledge, memory and communication, 72(3), pp.233-249.
- Antonio, A. and Tuffley, D., 2014. The gender digital divide in developing countries. Future Internet, 6(4), pp.673-687.
- Bailer, Savita., 2018 ‘Gender, Mobile, and Development:The Theory and Practice of Empowerment | Introduction’. Information Technologies.
- D'ignazio, C. and Klein, L.F., 2023. Data feminism. MIT press.
- Buolamwini, J. and Gebru, T., 2018, January. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77-91). PMLR.
- Noble, S.U., 2018. Algorithms of oppression. In Algorithms of oppression. New York university press.
- York, J.C., 2022. Silicon values: The future of free speech under surveillance capitalism. Verso Books.
- Benjamin, R., 2023. Race after technology. In Social Theory Re-Wired (pp. 405-415). Routledge.
- Nyamnjoh, F.B., 2019. ICTs as Juju: African inspiration for understanding the compositeness of being human through digital technologies. Journal of African Media Studies, 11(3), pp.279-291.
- McFarlane, A., Samsioe, E., 2020. #50+ fashion Instagram influencers: cognitive age and aesthetic digital labours. JFMM 24, 399–413. https://doi.org/10.1108/JFMM-08-2019-0177
- Digital activism e.g. the Risktakers Fellowship hosted by the Allianz Foundation [https://risktakers.space/]
- Youth-focused digital technology training programmes e.g. the Digital Day Camp hosted by Eyebeam [https://eyebeam.org/program/digital-day-camp/]
- Research Data MANTRA. https://mantra.ed.ac.uk/.
- Lehuedé, S. (2024). An alternative planetary future? Digital sovereignty frameworks and the decolonial option. Big Data & Society, 11(1).
- Gupta, S. (2020). Digital India and the Poor: Policy, Technology and Society (1st ed.). Routledge India. https://doi.org/10.4324/9781003010241

### Propose some creative and critical ways in which you can address some of the challenges:

Editor 1:
Digital and Online Platforms:
Utilize digital and online platforms to conduct research in environments where censorship or restrictions may limit access to traditional research methods.
Leverage social media, online surveys, and virtual focus groups to reach LGBTQ+ individuals.
Virtualized Sexual Orientation:
Conduct design research by blurring the interviewee’s sexual orientation (queerbaiting/straightbaiting). Circumvent government policy and access all interviewees as straight people
Editor 2:
1. Collaboration and Networking: developing the connections with institutions, organizations, or individuals who have access to the desired datasets.
2. Participation in Data Initiatives: participating in data initiatives that focus on specific research areas may provide access to shared datasets.
3. Advocacy for Policy Changes: engaging in advocacy efforts for policy changes that facilitate responsible and ethical data access can contribute to a broader shift in the research landscape.
4. Alternative Data Sources: exploring alternative data sources that are more accessible or publicly available can be a pragmatic approach.
Editor 3:
Gender-Sensitive/Fair Data Collection Training: Provide gender sensitivity training for data scientists, analysts, and relevant professionals to ensure they understand, consider, and appropriately handle gender-related data. Meanwhile, advocate for fair and inclusive data collection methods, ensuring datasets include diverse gender information to avoid stereotypes and biases.
Promote Tech Gender Equality: Efforts to narrow the technology gender gap, providing more opportunities for women to engage in digital technology and data science fields. This includes offering training, mentorship programs, and career development support.
Encourage Gender Diversity/Data Expertise: Advocate for gender diversity in data science teams, ensuring women are adequately represented in the development of data methods and skills to better address their needs. Especially when conducting research related to women/feminism, having the same-gender researchers are necessary. Also,encourage and support research and educational institutions to conduct professional training and research on gender data to cultivate specialized talent.
Develop Gender-Friendly Tech Tools: Encourage and support the development of gender-friendly technology tools, making them more adaptable to women's needs while ensuring the gender neutrality of technological solutions.
Digital Literacy and Skills Training: Provide digital literacy and skills training tailored for women to enhance their participation in the digital society, improve understanding of data science, and increase employment opportunities in related fields.
Emphasize Privacy Protection: Strengthen privacy protection for women, including ensuring compliance during data collection and processing and assessing risks that may lead to privacy infringements. Which also need to be noticed is the research ethics, the boundaries between personal privacy and research topics have to be carefully discerned from a gender perspective.
Editor 4:
* Clarify and Simplify the Informed Consent Process: Ensure that the elderly fully understand how their data is being collected and used. Use language and formats that are easy to understand, and provide additional explanations and support as necessary. For example, if dialect-speaking older people are involved, it is necessary to seek help from local researchers to help communication with older people.
* Provide Data Management Education and Training: Educate the elderly about data privacy, security, and their rights. This can be done through community centers, online resources, or family members. For example,
** Organize workshops and seminars in community centers focusing on data literacy.
** Develop easy-to-understand online tutorials and resources.
** Involve family members in educational sessions to create a support network.
Editor 5:
* Organising reading groups focusing on non-Western sources and literature and with a more international scope e.g. collaborative reading groups between the institutions in the Global North and the Global South
* Providing digital sources with non-hierarchical terminology e.g. stop using terms like ‘essential’ and ‘additional’
* Encouraging the use of digital tools/media to explore non-Western digital resources e.g. Language reactor, an AI translator that allows instant translation of different digital media [https://www.languagereactor.com/]
* Forming a network of academic researchers/scholars to lobby with publishers on the balance of jouranl issues/publications about Western and non-Western theories/thinkers/projects
Editor 6: Resource or tool kit for humanities researchers - the example is https://mantra.ed.ac.uk/
Training and discussions with software designers and policymakers - such as Brandwatch - to keep at forefront inclusivity and diversity of various cultural perspectives to cater global communities.