What's Love Got to Do With It? Stewarding a Healing Vision at the National Congress of American Indians Policy Research Center
Bibliographic record
Abstract
J O U R N A L O F A M E R I C A N I N D I A N E D U C A T I O N — 5 5 , I S S U E 2 99 Indigenous Policy Forum What’s Love Got to Do With It? Stewarding a Healing Vision at the National Congress of American Indians Policy Research Center malia villegas In the winter of 2010, I received a call from Sarah Hicks Kastelic, then director of the Policy Research Center (PRC) of the National Congress of American Indians (NCAI), headquartered in Washington, DC. I consider Kastelic a mentor, a trailblazer, and a second cousin twice removed, as we both have roots in the Native Village of Afognak in Southwest Alaska. We had collaborated many times over the previous years as part of her work at NCAI and my work with the Alaska Native Policy Center, both established in 2004 with similar missions, yet located thousands of miles apart. She told me she was leaving NCAI to take a role that would eventually lead to her serving as the executive director of the National Indian Child Welfare Association in Portland, Oregon. Shocked at first, because I had always associated the NCAI Policy Research Center with her leadership, I did not quite realize I had started to chuckle until she asked me why I was laughing. I told her that I pitied the fool who tried to fill the very large shoes she was leaving behind. She started to chuckle herself, so I asked her why she was laughing, and she said, “Well, because you’re that fool!” I was honored that she thought of me as she was preparing to transition to her new role, but I will admit this call came much earlier in my career than I might have anticipated. I had only just completed my doctorate earlier that year and was barely a year into my postdoctoral fellowship in Australia. With Sarah’s encouragement, as well as that of Greta Goto—a dear friend and former director of the Alaska Native Policy Center—I prepared my application for the position at NCAI. After two rounds of interviews , I was blessed to make it to the final round when dynamics with 100 J O U R N A L O F A M E R I C A N I N D I A N E D U C A T I O N — 5 5 , I S S U E 2 my research team in Australia came to a head over issues of cultural difference and leadership—we were working as the evaluation team for the first national, Indigenous education reform in Australia. I notified NCAI that I needed to pull out of the process in order to give my all to my research team. Two weeks later, I was sitting in a trailer that doubled as a hotel room in the Kimberley Region of Western Australia when my cell phone rang, at 10 p.m. Australia time. The woman on the other end of the line identified herself as Jackie Johnson Pata, executive director of NCAI. I was stunned, as I was not expecting to hear from her; certainly not while I was traveling remotely. She asked whether I would reconsider putting my name back in the running for the position . The situation with my Australian research team had not improved , and it did not look as though the team would be able to address the critical issues of race and cultural difference that I was concerned were necessary to include in our analysis. I told Jackie I would throw my hat back in the ring, but that I also had been invited to give job talks at two universities since exiting the NCAI process. In May 2011, I embarked on a whirlwind tour to Phoenix, Arizona; Vancouver, British Columbia, Canada; and Fairbanks, Alaska, to give job talks and visit with scholarly colleagues. On the last leg of my journey , I was sitting in Alaskaland in Fairbanks, Alaska, where they host a salmon bake outdoors for tourists. About 30 of us were being hosted at an international...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".