Forecasting Employment Growth Along the GTA West Corridor: A Spatial Approach to Planning and Applied Policy Research
Bibliographic record
Abstract
The objective of this research paper is to forecast employment based on the suitability of available land (supply) along the proposed GTA West Corridor (demand), a highway which links the existing highways within the Greater Toronto Area (GTA). The theoretical framework and proxy sites establish the relationship between employment land uses, highway exits and interchanges (impedance). Using Spatial Analyst tools, the 2019 Land Cover data is analyzed (Step A) to identify the 'Whitebelt/Excess lands’ by each municipal jurisdiction which assesses that Caledon (9,800 ha) has the highest followed by Municipality of Halton Hills (7,000 ha), Brampton (4,900 ha), Milton (4,800 ha), Vaughan (4,700 ha), Mississauga (1,000 ha) and King (8 ha). Suitability Model (Step B) is executed converting the vector data to raster data, and considering the Euclidean distance criteria from the highway, exits and interchanges. The total identified suitable employment lands along the corridor is 5,482 ha towards employment use, out of which 2,473 ha is in primary, 1,733 ha in secondary, and 1,276 ha in tertiary distance from the proposed highway, exits and interchanges. The forecast model (Step C) estimates 87,800 jobs (Scenario #1), 58,400 jobs (Scenario #2) and 29,000 jobs (Scenario #3), in line with the regional/municipal employment density targets and achieve desired employment activity by 2031. These results help policy makers in identifying the strategic direction towards the economic benefit that can be achieved by designation of employment lands (highly recommended with special policies to limit the land use conversions) along the proposed highway corridor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".