Temporal GIS and Statistical Modelling of
Bibliographic record
Abstract
Spatio-temporal data models for describing complex lifelines and trajectories of persons, as well as events that affect their evolution, are prerequisites for the statistical analysis of their relationships. Such analyses are useful to develop a better understanding of urban dynamics and social transformations. This paper develops a spatio-temporal database model for handling personal trajectories along a time line (many complementary lifelines) allowing for the statistical analysis of any pre-defined event. It combines survival analysis, Cox regression and temporal GIS. The combination of these aspects support an assessment of the likelihood of any event to occur in the life of persons at risk, after a given time delay and under some specific conditions. Our model was implemented and tested using a geo-relational approach that also supports spatial and temporal reasoning at complementary levels of abstraction. It allows the cross-analysis of several multi-dimensional lifelines to form individual trajectories. The application example is based on an historical survey of personal biographies (spatially located) of 418 professional workers living in the Quebec Metropolitan Area in 1995-96.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".