How Waitlist Management Biases Data Production in Built for Zero Communities
Bibliographic record
Abstract
Built for Zero (BFZ) is a new data-driven approach to allocating housing assistance to homeless households. BFZ implementation includes the production/maintenance of a “by-name list” (BNL). The BNL is a regularly updated spreadsheet with personalised data about everyone who enters and exits a local homeless system (i.e., flow). Local administrators use the BNL to produce “by-name data” (BND). BND lets administrators monitor and manipulate system flow in “real time.” Scholars have not analysed how administrators manage their BNL and produce BND. This paper starts that conservation by answering three questions with interview data that was collected from administrators at 28 “BFZ communities” (i.e., homeless systems implementing BFZ): How do administrators manage their BNL? What factors influence BNL management? How does BNL management affect BND accuracy? Using thematic analysis, the author provides evidence that administrators manage the length of their BNL by removing “inactive” clients who have disengaged from service providers. Administrators try to verify housing status before deeming someone inactive. The verification process is shaped by social policies, network ties, virtual interactions, and administrative discretion. Because those things vary across homeless systems, administrators face different constraints and enablers whilst verifying someone’s housing status. The intersection of contextual factors and subjective processes biases the BND administrators produce from their BNL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".