Dr. Joan LaFrance of Mekinak Consulting in Seattle, WA, spoke about Indigenous evaluation. Joan was a trooper. Her flight was delayed due to weather and she didn't get into town until the wee hours of the morning and still made it to deliver an endearing talk and share her vast knowledge of working as an evaluator in Indian Country. She became an evaluator because of her first evaluation experience. At first, she had a negative reaction because she didn't want to have an external evaluator coming in and judging her program. Despite this, she did realize the value in the data that came out of the evaluation and decided she wanted to get her doctorate degree, become an evaluator, and take that back to Indian Country.
I loved everything that Joan said and, like the rest of the attendees, absorbed all of her words. When you get data and do evaluation, knowledge is created, and Indigenous knowledge includes a few different kinds of knowledge: traditional knowledge (i.e. creation stories); empirical knowledge (i.e. farming); and revealed knowledge (i.e. knowledge that comes through dreams or ceremonies). Knowledge exists to help us walk on "moral path" when we gather data we want to give it purpose and data should never be gathered for data-sake.
Some great insight from Joan about evaluation is that you should never classify things too quickly when you are doing evaluation in Indigenous communities. At times, evaluators in Indigenous Communities be seen as not respecting values of the people they're evaluating. Joan stressed that everything we do has value and it must be interpreted and evaluated appropriately because data has power and it must be used in a good way. She encouraged tribes to explore their epistemology or ways of knowing that they want to guide their research and evaluation endeavors.
Joan gave a great overview on the differences between Western and tribal ways of developing programs. Through data, we can understand ourselves. In Western thinking and doing, theories or programs are tested and if they work in enough places, they try to say that they are generalizable and can be implemented anywhere. This is not the approach to data for tribal communities because of how unique each tribe is. Tribal communities want to understand themselves in their own way, context, and situations. In addition to this issue of generalizability of programs that are evaluated, data gathering and interpretation must be inclusive. There can't be one or two people looking at data and figuring out what it means, which is typical of Western ways of data analysis; there must be many people involved in this effort. Lastly, Joan talked about the importance of not reducing people to a statistic and instead include multiple measures so that there is a full picture of the people being evaluated.