Bibliographic record
Abstract
We are pleased and honored to include the keynote address delivered by award-winning Sičáŋǧu Lakota artist, Dyani White Hawk Polk at the Native American Art Studies Association Conference (NAASA) on 2 October 2019. The NAASA is the leading professional and scholarly organization supporting and promoting the study and exchange of ideas related to Indigenous arts in the United States and Canada. At the organization’s biennial conference in Minneapolis, Minnesota, and while standing on Dakota traditional lands, Dyani White Hawk Polk delivered her important address, “The Long Game.” In it, she movingly and powerfully explores her life experiences, the history of and ongoing effects of colonialism, and how both inform her artistic practice. Her address traces the roles of mentors in her life, including the late Ho-Chunk artist, Truman Lowe, who taught at the University of Wisconsin, Madison during her time in the MFA program. She eloquently speaks to the challenges she has faced in tackling head-on hierarches in the art world that have continuously sought to diminish the significance of Indigenous art. She also provocatively addresses how artists, scholars, and critics can build the field of Indigenous art and support Indigenous artists. The address was widely praised at the conference, owing to the power and beauty of her words, as she spoke to how the past effects the present and as she illuminated a path for the future. We are grateful to be able to include her address in this Special Issue of Arts journal. Her thought-provoking address is both an artistic statement and a profound and moving commentary on the state of the Indigenous art world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.207 | 0.066 |
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 source (direct Gemma or distilled Codex), 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".