<i> An Intersectional Approach to Homelessness: Discrimination and Criminalization <i>
Bibliographic record
Abstract
The purpose of this essay is to address discrimination against homeless people. First of all, the theory of intersectionality will be explained and then applied as a method of analysis. The complexity of defining homelessness will be tackled, focusing on the difficulties encountered when approaching this concept. I will discuss notions of protected ground and immutability of personal characteristics, then outline an intersectional approach to homelessness. Intersectional discrimination has not yet been applied by many courts and tribunals, but Canada has proven to be a vanguard in this area. For this reason, Canadian case law has been chosen as the main example in this research. I will explore stereotyping, prejudices, and social profiling in connection to homelessness. In addition, I will touch on a peculiar aspect of homelessness that is concerned with the representation of different minority groups (such as race, mentally-ill and so on) within the homeless population. Different laws and other legal sources concerned with criminalizing specific conducts against public order will be analyzed applying the outlined intersectional method. Specifically this work will concentrate on quality of life regulations and anti-homeless regulations. This paper will then establish that homelessness is a ground worthy of protection and then it will argue that the aforementioned kind of legislation results in direct and indirect discrimination. In conclusion, the arguments in favor of including homelessness or social condition as a ground of discrimination will be laid out, with reference to Canadian, European, and international law sources.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.045 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| 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".