OTEM 2032: \nCatalyzing the Disruption of Transportation Equipment Manufacturing in Ontario
Bibliographic record
Abstract
Artificial Intelligence (AI) has created uncertainty. This uncertainty of the impact that AI could have on jobs, people, and life as we know it is often met with sensationalized suggestions such as Elon Musk’s assertion that AI is humanity’s biggest threat (Higgins, 2017). However, I believe that AI is a technology and merely a tool; it is not a force of nature. Hence, as a resident of Ontario who works in the manufacturing industry, I asked: \n“What might we expect of the manufacturing activities of Ontario's Transportation Equipment Manufacturers (OTEMs) during the emergence of AI in the next 15 years?” \n I examined this question through a literature survey, scenario planning, and impact analysis. \nI developed a scenario-planning matrix that included the two most critical uncertain drivers surrounding OTEMs’ business activities in Ontario. These drivers were AI’s ability to significantly disrupt the human labour requirement and the outsourcing of manufacturing activities. I explored the relationship between these two drivers and how scenarios could be used to develop resilient strategies. Next steps included using the uncovered parameters in a strategic planning process for OTEMs to develop strategies and create a culture that embraces resilience. \nKey words: AI, manufacturing, automation, Ontario, scenario-planning tool
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".