Lessons from the Survivor Inclusion Initiative (SII) in the UK, US, and Canada
Bibliographic record
Abstract
The Survivor Inclusion Initiative (SII) is a financial access project launched in September 2019 in the UK, US, and Canada by the Finance Against Slavery and Trafficking (FAST) Initiative at the United Nations University Centre for Policy Research. SII brings together financial institutions and survivor support organizations (SSOs) to work towards a shared aim of facilitating survivors’ access to basic banking services, such as checking and savings accounts. This close collaboration enables safe and reliable engagement with survivors, and has resulted in changed banking practice such as trauma-informed practices and simplified or alternative customer due diligence. The SII is now in its third year of operations. To mark this anniversary and learn from challenges and successes so far, an independent Expert Review was commissioned by FAST and took place between November 2021 and April 2022. The Review aims were to help advance the SII's goals of achieving financial inclusion of human trafficking survivors and increasing stakeholder participation. The following briefing highlights some of the insights that were gathered.
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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 | 0.067 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".