A New Canadian Pastime? Counting Homeless People: Addressing and preventing homelessness is a political problem, not a statistical or definitional problem
Bibliographic record
Abstract
How many homeless people are there? In Toronto? In Canada? Who knows? No one knows. For some, it seems, trying to count them is more important than taking action. There is agreement on two observations: the number is very large (measured in the many thousands, not the dozens or hundreds), and the number is increasing. Discussions of the number of unhoused people usually mix two very different questions: How many unhoused people are there right now (that is, on a given day or night). And how many people have been unhoused over a given period-of-time (that is, how many people are affected by the problem). The first is called a ‘point prevalence’ measure (a point-in-time count) and the second is called a ‘period prevalence’ measure. We already know that it is impossible to count a mobile population that lacks a permanent address. All our statistics about people and their households start with their address – their fixed location. Housed people may decide to move from one fixed location to another, but they always have an address. Unhoused people do not. Even if we take the time and resources to produce a somewhat defensible estimate we remain stuck with a final question: so what? What difference will such a point-in-time count make? Who will do what with the number? How many houseless people will be better off as a result? Those who are currently unhoused need to be adequately, affordably, and securely rehoused as quickly as possible. Those who are at risk of becoming houseless need measures that will prevent that outcome. We already know more than enough about the nature and magnitude of the problem to embark on rehousing and prevention programs. Addressing ‘homelessness’ is a political problem, not a statistical or definitional problem.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".