Industry 4.0, the Future of Work and Skills: Building Collective Resources for the Canadian Aerospace Industry
Bibliographic record
Abstract
Prior to the [Coronavirus Disease 2019] COVID-19 pandemic, demand for labour outstripped supply in the industry, resulting in labour shortages in many occupations. A major ongoing challenge is attracting a new generation of workers by offering good jobs and better work. The adoption of Industry 4.0 (I4.0) is often presented as a way to increase the competitiveness of the industry, while improving the quality of work and increasing skills by reducing repetitive, routine tasks. This research in the Montreal and Toronto aerospace clusters has two objectives: (1) to better understand the impact of I4.0 on work and skills; and (2) to identify the conditions that will enable the various stakeholders to meet the challenges of I4.0 and future skills. The research found that there is much variation between firms in terms of I4.0 adoption. Some firms are fully engaged and are currently operating a virtual factory, whereas others have yet to begin the turn towards I4.0. The impacts of I4.0 on work and skills vary, and they do not affect all workers nor affect them all in the same way. In both clusters (Montreal and Toronto), the central challenge of I4.0 and future skills is the production of collective resources.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".