Why Windsor deindustrialized differently than Detroit
Bibliographic record
Abstract
This paper examines the divergent trajectories of automotive investment and employment in Detroit, Michigan and Windsor, Ontario. Located on opposite shores of the Detroit River, in the United States and Canada respectively, Detroit and Windsor are the founding cities of the North American auto industry. Long dominated by the Big Three, their factories have produced vehicles for the same continental market since 1965. Each has weathered parallel challenges since then, including spikes in the price of oil, the Big Three’s loss of market share, the transition to lean production, and the near-collapses of Chrysler and GM. Yet Detroit began deindustrializing decades earlier and lost much more employment than Windsor. To determine why, we compared their automotive sectors from 1900 to the 2010s. Since the Depression, each city has repeatedly confronted the prospect of deindustrialization, but three factors have made Windsor more resilient: (1) federal and provincial interventions on its behalf, (2) Windsor’s greater competitiveness with respect to factor costs, quality, and innovation, and (3) Windsor’s annexation of outlying territory to capture new factories. These differences show how national, subnational, and regional/local policies have mediated corporate decision-making to produce a variegated North American Rust Belt, with Canada outperforming the United States.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".