Bibliographic record
Abstract
The study of urban ecology cannot be separated from geographical space; however the limitation of access to spatio-temporal information is a reality. Creating a crime information system for the Maltese Islands has entailed bridging the gap between analogue social information and spatial planning information which rarely talk. This paper covers the process employed to initiate an understanding of the legislative and operational tools available to crime and security geographers through to the preparation for the launching of country-wide baseline datasets for effective future socio-technic analysis. The decade long process to implement a major project using ERDF funds is at the final stages prior to the initiation of cross-thematic studies that span the physical and social domains. Both environmental and green criminology is now set to take off employing one of the most comprehensive GI systems spanning urban and rural offences (person and property-oriented), census data together with the natural, social and physical environments. The study reveals issues on access to data, mitigating processes undertaken and the forward planning initiatives to ensure free dissemination of environmental data to the academic and general public. Initial studies based on the analysis of crimemaps, poverty and crimes related to the environment show correlation between the different social and geographical spaces.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.012 |
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".