In this issue: Offshoring And Immigration – The Impacts On IT Workers In Canada And The United States
Bibliographic record
Abstract
Abstract: The growth of employment in the IT field has been a significant phenomenon in the market place, with IT workers now making up over 3 % of the workforce and perhaps another 10% holding IT-related jobs. Following 30 years of almost continuous growth, employment in the field fell in the recent recession. While future projections of employment growth are significant, in Canada and the USA, the economic recovery has not produced a return to the previous levels of growth in the IT field. Enrolment in university computer science and IT programs is down. Some observers hold that key contributors to the lack of growth are the impacts of the offshoring of IT work and the role of immigration, and that these factors make government predictions for growth in IT work unrealistic. This paper examines the impact of immigration and offshoring on the supply/demand situation for IT workers in North America, drawing from both the Canadian and the US experience. Preliminary conclusions suggest that the growth of IT work will continue but in a different pattern than in the past and that immigration policies are having an impact on the evolution of the IT discipline and that offshoring, while having limited overall impact on levels of IT employment, may
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".