A collective awareness platform for missing children investigation and rescue
Bibliographic record
Abstract
The plight of missing children is particularly strenuous and sensitive for societyat local, national and European levels, cutting across class, race,and age. A quarter million cases of missing children is reported in the EUannually, which are eitherparental abductions, stranger abductions, runaways, missing unaccompanied migrant minors, and generally lost, injured, or otherwise missing children. The problem of missing children is a complex, multi-faceted phenomenon, comprising legal, psychological and sociological aspects, which are complicated further due to the strong emotionsfrom the close environment of the missing child. This paper presents the challenges missing children investigation and rescue currently faces, and proposes a solution that uses ICT, advanced analytics and collective intelligence,to achieve more rapid and effective resolutions. The proposed methodology leverages the untapped potential of open, social, and linked data to augment the background information ofmissing children,through multi-layer -personal, psychological, social and activity -profiling and predictive analytics, respecting and protecting privacy,and personal data. Usinglocation-based mobile notifications that spread using geo-fencing, citizens close to the place a missing child was last seen or is more probable to be found become “social sensors” for the investigation, contributing and validating potential pieces of evidence. Through the EU-funded project ChildRescue, the proposedsolution is currently at the last phase of its development and aims to be adopted by different voluntary organisations, according to their needs and the readiness of their systems and processes. The project’s results are nowpiloted in missing children cases by organisations responsible for the Amber Alert,and the 116 000 pan-European hotline, as well asunaccompanied minors’ cases supported by the Hellenic Red Cross.The resulting collaboration platform and mobile applicationswill be publicly launched in 2020.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".