Bibliographic record
Abstract
Native American and Indigenous peoples are the original inhabitants of the U.S., including hundreds of tribes with distinct cultures and histories that inform their epistemological (i.e. ways of knowing) and ontological (i.e. ways of being) worldviews. Despite this, Western peoples continue to <italic>story</italic> the experiences of Native peoples for them, creating master narratives in which Native peoples are relics of the past, and impoverished, uneducated, drunkards in the present. Indeed, this is a longstanding effect of the colonial project that continues to erase the authentic, lived experiences of Native peoples, thus limiting their self-understanding and future possibilities. To counter these enduring colonial narratives, it is critical Native peoples have access to both <italic>Native-centered</italic> and <italic>community-based</italic> spaces in which the complexities of Native identities and experiences can be voiced and storied. <italic>OrigiNatives</italic> sought to provide urban Native peoples in Minnesota a space that privileged their ways of knowing and being through digital storytelling workshops, in which Native peoples (<italic>n</italic> = 75) from thirty-four different tribal communities created original and authentic stories of their cultures, histories, and lives using digital media technologies. This chapter reviews the process of creating and implementing <italic>OrigiNatives</italic> through partnerships with Native-serving organizations in Minnesota, highlighting implications that digital storytelling methodologies have for challenging and resisting coloniality by empowering Native peoples to tell <italic>our stories, our way</italic>.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.021 | 0.024 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".