Locating Undocumented Migrant Populations: A Case Study Using Statistical Data of Cork City of Sanctuary, Ireland
Bibliographic record
Abstract
Perilous countries often produce citizens who will risk becoming undocumented in a foreign country in order to seek safety. However, the term undocumented migrant includes many sub- categories such as trafficked individuals, students who have outstayed their visas or those who unintentionally transitioned from legal migrant to undocumented migrant. Cork City of Sanctuary was created to protect all communities including documented and undocumented migrants living in Cork City Ireland. Government and social service institutions that have been established for migrants are only beneficial if they are spatially accessible to highly-vulnerable migrant populations. This study utilized interview data, a Principal Component Analysis and a K-Means Cluster analysis to create a highly-vulnerable migrant population segmentation of Cork City of Sanctuary. As a result, the study found that creating segmentations of highly-vulnerable migrant populations Cork City was possible however, the available statistical data was limiting considering migrants have many sub-categories in which government census data does not capture, there was a lack in variety within the various census variables, and available open source data does not provide statistical information on undocumented and documented migrants collected from sanctuary colleges/university or sanctuary hospitals within Cork City of Sanctuary.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".