Indigenous Spaces in Urban Places: A Documents-Based Inquiry into the Indigenizing Geographies of Urban Indigenous Organizations
Bibliographic record
Abstract
This dissertation will tell you two interrelated stories. Firstly, this project will tell a story about how the urban Indigenous community in Cataraqui/Katarokwi/kingston Indigenizes lands and spaces claimed and occupied by settler/non-Indigenous people, using documents, virtual presence, and other material available in the public domain. Secondly, this dissertation tells the story of my family and how we have navigated through the foggy territory of Indigeneity, passing for white, residential “school,” love, abuse, and dysfunction Kingston. Using Indigenous research methods and presented as a dialogical discourse, I’m talking to you, the reader, both about the general and the personal stories rather than presenting a western dissemination of facts. This project provides a springboard for community-based research with urban Indigenous communities and their Indigenizing practices, should there be interest in such a project. It also demonstrates how allowing Indigenous epistemologies to guide research can lead to unsettling the status quo within the academy and generate research/stories that speak to Indigenous cosmologies, heart knowledge, land relations, and lifeways. This opens new possibilities for Indigenous research within the academy. Indigenous Peoples are reconnecting to their ancestral communities, languages, and cultures and building from them to create urban Indigenous communities that deal with contemporaneous issues facing Indigenous Peoples. Rather than passively waiting for the government to address such things as homelessness, hunger, addiction, and the loss of culture/language/land attachments (for example) that affect Indigenous Peoples disproportionally, urban Indigenous communities are using their resilience and cultural knowledges to tackle these (and other) issues themselves. Let’s discover how this is happening together! This story will begin at the beginning and the content will flow as it develops itself.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".