Demographic and Spatial Dynamics: Impact on Transportation and Future Issues
Bibliographic record
Abstract
After a brief overview of the main spatial and historical trends in the settlement of Quebec, this report discusses the connections between the dynamics of urban and interregional demographics. The post-war baby boom is then examined from the perspective of the scope of the fluctuations in the birth rate and the number of births before and after the baby boom, in order to clearly explain the medium- and longer-term impacts of this phenomenon on the age structure of Qubec's population and the trends that affect it. These trends and their impact on the demand for transport are then scrutinized by cross-analyzing demographic statistics and data pertaining to driver's license holders, broken down according to gender and age group. By demonstrating that the cohorts of baby-boomers reaching adulthood have swelled the number of potential drivers, this analysis sheds light on a major contributing factor to the very sharp increase in traffic that has been seen in recent years, despite the fact that demographic growth has tapered off significantly. Looking into the future, the aging of these cohorts of baby-boomers will continue to be the predominant demographic phenomenon, and the available forecasts lead to the conclusion that the population trend of concentration in the southwest of the province will likely persist. The report concludes with the determination that this concentration is not likely to produce any tangible benefits in terms of transportation demand from the aging population, because the regions where the traffic load is heaviest should continue to grow, and should be the least affected by the aging. For the covering abstract see ITRD E139491.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".