Restoring Personhood through Peer Hosting: The Peers Immigration Rehabilitation Module (PIRM) as a Scalable Rehabilitation Model for Immigrants and Refugees
Bibliographic record
Abstract
The contemporary global migration apparatus is currently characterised by a profound administrative and humanitarian flaw: the prolonged, traumatic transit period between leaving a home country and achieving systemic acceptance in a new nation. This institutionalised waiting period, often stretching from 6 to 24 months in bureaucratic limbo, frequently inflicts severe psychological, emotional, and financial damage on immigrants, stripping them of their dignity and autonomy (Portes & DeWind, 2007; WHO, 2021). Conventional state-led rehabilitation models rely on isolationist detention centres or fragmented NGO silos, treating displaced individuals as economic burdens rather than possessing inherent social value. This manuscript introduces the Peers Immigration Rehabilitation Module (PIRM), a core component of the Alam Happy Town (AHT) living laboratory framework, designed to structurally eradicate this transit trauma. Operating on the philosophical and biological principle of "personhood," PIRM bypasses institutional isolation by operationalising a direct, family-to-family sponsorship model. AHT residents can directly sponsor friends, family, or vulnerable individuals who reach out via designated web portals. Because the AHT community’s internal economic engine, the Daily Sustenance Distribution System (DFDS), absorbs 100% of the immigrant’s financial burden upon arrival, the state faces zero economic liability. Consequently, this paper advocates for a policy shift allowing expedited government processing, such as a 1-month visit visa for AHT-sponsored individuals. By synthesising well-being economics, theories of recognition, and rapid housing deployment, this research demonstrates that immediate peer-to-peer integration structurally preserves immigrant personhood, transitioning them from a state of systemic paralysis to immediate communal and economic empowerment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".