Please tell me you have published a description of your Data Equity Framework? Preferable in a peer-reviewed outlet? I am writing a paper on health equity in marginalized and multiply marginalized populations and will call on researchers to follow your data equity framework. Please tell me someone has published something on this? If not, will you please consider it? Even a brief editorial or essay, like Gaddy and Scott’s 2020 Urban Institute paper, would be incredibly impactful.
I agree - my colleagues and I would like to bring these ideas to the educational assessment community too. The “old guard” still is stuck on seeing peer reviewed citations.
I am probably more staid than I would like to admit but I see real value in pushing traditional academic researchers and funders toward data equity innovation. In my field, important research is published everyday to advance health equity in intersectional marginalized communities. Sadly, very little attention is paid to fundamental data equity issues, which limits the value of these findings. Peer-reviewed publications are needed to communicate and influence research.
As an academic researcher, it would be incredibly helpful to have a citation to include in grant proposals and publications. The Data Equity Framework is so critical to our work in health services research and I’d love to be able to share more broadly with my colleagues!
Hi there friends We hear you, and we are working on it. We have very mixed feelings about traditional academic publishing and are in deep discussion about it with our advisors. We really value the community and want to provide tools and support while also ensuring we don’t become part of “the problem”
As a result, we do have several pieces forthcoming in a variety of venues.
Currently, there is a peer-reviewed pre-print that is citable here. We’ll keep you updated as we publish materials.
The Data Equity Framework: A Concrete and Systematic Equity-Oriented Approach to Quantitative Data Projects
Thank you so much, Heather and Lashawn, for this wonderful summary of the Data Equity Framework in a format that I can leverage in my academic position! This is very helpful.
This is an interesting thread not only about the Data Equity Framework (thanks for sharing, Heather!) but also more broadly in terms of thinking about the best journals to have these conversations. As a recovering academic, I don’t have the pressure to publish. However, many who do this work in academic spaces do, and I am really curious about folks’ thoughts about where these might fruitfully be placed.
This is a great question, jenrose, about what academic spaces should be leveraged to advance data equity. I do not have a journal recommendation but would LOVE to see data equity methodology discussed at the Council of Professional Associations on Federal Statistics (COPAFS)!
The Council of Professional Associations on Federal Statistics (COPAFS) is devoted to educational activities and preserving the public good represented by federal statistical collections. Since 1980, COPAFS has provided an open dialog between those who use federal statistics in professional contexts and the Federal statistical agencies that produce those statistics for the public good. Supporting organizations include professional associations, businesses, research institutes, and others that help to produce and/or use federal statistics. Our Goal: Advancing Excellence in Federal Statistics. COPAFS’ objectives are to: • Increase the level and scope of knowledge about developments affecting Federal statistics; • Encourage discussion within and among professional organizations to respond to important issues in Federal statistics and bring the views of professional associations to bear on decisions affecting Federal statistical programs.
COPAFS is hosting a research and policy conference in October 2023 that features several equity discussions, https://static1.squarespace.com/static/5d0bbb7c645c4200015e02de/t/6514abc40bb58c52a35507ad/1695853509707/FCSM+2023+Program_Prelim+v11.pdf
Thanks for the tip! I will check it out.