The Design of an Efficient Offshoring Strategy: Some Reflections and Links to SNC-Lavalin
Bibliographic record
Abstract
The objectives of this paper are threefold; First, to brush an overview of the underlying drivers of the offshoring phenomenon that have appeared on economic and public policy radar screens over the last 15 years; Second, to look at the recent offshoring experience of a large Canadian engineering firm (SNC-Lavalin) with significant international experience and exposure; Third, to draw from the analysis, the evidence and the case at hand some lessons for public policy aimed at defining winning offshoring strategies. L'objectif de ce rapport est triple : d'abord, dresser le tableau des facteurs sous-jacents au phénomène de l'impartition offshore devenu un sujet de préoccupation en politique publique; ensuite, considérer l'expérience récente d'une grande entreprise canadienne d'ingénierie (SNC-Lavalin) en cette matière; finalement, inférer de ces analyses, de ces données et de ce cas, des leçons pour la définition de politiques gagnantes d'offshoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".