Expanding engineering practices: immigrant accounts of innovation from a practice-based perspective
Bibliographic record
Abstract
Research shows a positive association between skilled migration and innovation. Related literature however is largely limited to the use of proxies such as patents, and publications. There is also a lack of attention to how innovation is accomplished in practices. This paper addresses these gaps with an examination of the innovative contributions made by immigrant engineers in Canada. Conceptually, informed by practice-based theories, it conceives innovation as a sociocultural and sociomaterial process that leads to the transformation of the object/motives of activities, i.e. the problem space to which actions are directed. Empirically, drawing on a thematic and situational analysis of the career accounts of 32 immigrant engineers, it shows that immigrants expand engineering practices by introducing, inter alias, new technologies, products, processes, policies and standards. It further traces the rise of the problem spaces, and the ways in which engineering objects and other practitioners are knotted into practices of innovation. It argues that while immigrants manage to introduce epistemic objects through continuous learning and knowledge translation, it is through the enrolment of other practitioners, and technologies and tools that relations of differences and power are (re)negotiated, and new ways of doing become amplified as innovation at work.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".