Bibliographic record
Abstract
WINNER Canadian Historical Association Indigenous History Book Prize (2022) WINNER Canadian Historical Association Ontario CLIO Prize (2022) WINNER Ontario Historical Society Indigenous History Award (2022) WINNER Manitoba Book Awards Alexander Kennedy Isbister Award for Non-Fiction (2022) COMMENDED Labriola Center American Indian National Book Award (2021) Dadibaajim narratives are of and from the land, born from experience and observation. Invoking this critical Anishinaabe methodology for teaching and learning, Helen Agger documents and reclaims the history, identity, and inherent entitlement of the Namegosibii Anishinaabeg to the care, use, and occupation of their Trout Lake homelands. When Agger’s mother, Dedibaayaanimanook, was born in 1922, the community had limited contact with Euro-Canadian settlers and still lived throughout their territory according to seasonal migrations along agricultural, hunting, and fishing routes. By the 1940s, colonialism was in full swing: hydro development had resulted in major flooding of traditional territories, settlers had overrun Trout Lake for its resource, tourism, and recreational potential, and the Namegosibii Anishinaabeg were forced out of their homelands in Treaty 3 territory, north-western Ontario. Agger mines an archive of treaty paylists, census records, and the work of influential anthropologists like A.I. Hallowell, but the dadibaajim narratives of eight community members spanning three generations form the heart of this book. Dadibaajim provide the framework that fills in the silences and omissions of the colonial record. Embedded in Anishinaabe language and epistemology, they record how the people of Namegosibiing experienced the invasion of interlocking forces of colonialism and globalized neo-liberalism into their lives and upon their homelands. Ultimately, Dadibaajim is a message about how all humans may live well on the earth.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.603 | 0.215 |
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".