<i>Wanitoon ani Mikan Odenang</i>: Anishinaabe Urban Loss and Reclamation
Bibliographic record
Abstract
This article focuses on the loss of identity through cartographic colonization. From an Anishinaabe perspective, many of the most diverse urban centers in the Great Lakes region of North America are currently located in bays, along shorelines or at the confluence of lakes and rivers. Over time these places have changed, yet many of them have remained for centuries. Identifying some of the oldest cities before and after colonization, a period known as the time of disruption, reveals a spectrum of ideas related to the experience of loss, which in Anishinaabemowin is wanitoon, and the act of reclaiming and remembering, which is mikan. Using multiple languages and genres, offering definitions, descriptions and several poems originally composed in Anishinaabemowin and translated into English, this article asks questions about history through the lens of other languages and cultures. This methodology challenges us to see how cities are shaped by relations with the human and other-than-human world and demonstrates how cities are interconnected points. By revealing the names lying underneath colonial-era maps, we are reminded of the connections that shaped Indigenous ancestral practices, contemporary realities, and future possibilities for reconciliation. Anishinaabemowin is used as a means of historiography to trace the genealogy of urban centers and reveal the process by which the colonial landscape was constructed. By foregrounding Anishinaabe ontologies and poetics we can map reparation and social healing. As we are faced with extinction or evolution it is important to study Indigenous languages and philosophies as we seek ways to survive.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".