Bibliographic record
Abstract
Today, Brampton suffers from a negative image. \nEspecially Brampton’s majority of working-class \nimmigrants who are experiencing socio-economic and territorial exclusion. The exploitation \nby the media has also had an effect on the negative identity construction. \nWith a population close to 600,000 people, \nBrampton has many people to serve. Of the \n600,000 there are 234 different ethnic groups \nspeaking 115 different languages. \nBrampton’s First Cultural Master Plan is supposed to set the strategic direction for arts and \nculture in the city. My critique is that it misses the point. The under-presented Brampton \nyouth (who are mostly black and brown) to me \nis Brampton’s heart and soul - the youthfulness, \ncultural diversity and entrepreneurial energy \nought to be the most important resource for \nthe city. This said, Brampton’s focus on business \nand economic development is but one of many \nexamples of urban design that neglects the ethnicity and diversity of immigrant Canada. \nBrampton’s identity is growing, maturing, diversifying and transforming. As the city grows \nand matures, so are the residents. We live in a \nworld where everything and everyone is being \nconstantly classified into categories: religion, \ngender, ethnicity and race. The malicious use \nof racial categories has resulted in violence and \nracism. And most of the violence has been directed at First Nations Peoples, Black Canadians \nand immigrants from non-European countries. \nI argue that Brampton will continue to face racial tension unless something is done to the \nnegative representation and misrepresentation \nof cultural diversity. I think the energy ought to \nfocus on young people as they themselves are \ndefining their identities and wanting to make \nBrampton their own. My thesis, thus, begins \nwith the question: How can architecture bring \ncommunities together to co-exist while acting \nas a setting for self-identity formation, especially for youth in the city
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.719 | 0.465 |
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".