Shifting Hearts and Minds: Practical Communications Strategies for Addressing Homelessness in Mid-Size Cities
Bibliographic record
Abstract
The visibility of homelessness is increasing in mid-size cities, putting municipalities under mounting pressure to address this complex issue. Unfortunately, community sentiment and responses to homelessness are often informed by misinformation and divisive narratives. Communication strategies, therefore, play a key role in educating audiences, highlighting shared values across the political spectrum, and advancing human rights-based and sustainable solutions to homelessness. In this article, we offer strategies for educating, uniting, and mobilizing different stakeholders around human rights-based approaches to homelessness. We discuss how to segment, target, and reach different audiences with values-based and research-backed messaging, particularly through a layered approach. We share practical tools on how, when, and where to most effectively deploy content to maximize its reach. We also discuss best practices for communicating with any audience on the critical homelessness and human rights issues facing mid-sized communities today. These strategies and best practices will allow you to communicate with purpose, make the best use of resources, shift hearts and minds, and ultimately advance your goal of promoting human rights-based and sustainable solutions to homelessness in your community.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".