Bibliographic record
Abstract
This chapter engages with the Internet as a discursive realm where the profound conflicts generated by settler-colonialism continue to be played out. In the dynamic space of the Internet, Indigenous digital storying is a claim to rhetorical sovereignty that articulates Native self-determination through specific ways of knowing and being. A powerful statement of Indigenous self-determination is the use of mobile locative media – “Mapping Indigenous LA” (Gabrielino/Tongva and Tataviam), Knowing the Land beneath Our Feet (Musqueam), and Finding Sacred Ground (Lakota) – to create a digital remapping of Native geography, denaturalizing settler cartographies and restoring Native storying to the land, while two dominant “digital Native” forms of storying that use the capabilities of virtual media in conjunction with traditional literary genres to instantiate Indigenous cosmologies are the digital film-poems or “poemeos” exemplified by Heid Erdrich’s Anishinaabe storying, aas well as Elizabeth LaPensée’s Anishinaabe/Métis storying through Indigenously-determined digital narrative or videogames.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".