Factors Impacting the Supply and Demand of IT Workers in Canada and the USA
Bibliographic record
Abstract
From its early post-Second World War beginnings, IT employment has risen steadily, with over 3 % of North American workers now holding IT occupations and perhaps another 10 % working in IT related or IT-enabled fields. Since the mid-1980s, there have been reports of shortages-- both of IT workers and of specific IT skills. This long growth period was dramatically affected during the "recession " that took place at the beginning of the 21st century, when the IT industry and IT jobs were more significantly affected than other areas of the economy. Today, we find contradictory reports of continued unemployment and slower growth, along with the resurgence of predictions of labour and skills shortages. To some degree it seems to have been a "jobless " recovery. Enrolment in university computer science and IT programs is down dramatically, offshoring of IT work in on the increase, and questions are being raised about the role of immigration, despite government predictions for growth in most IT work! This paper is an attempt to build a comprehensive picture of the supply/demand situation 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 current responses are inadequate to meet the current challenges. Without action by industry, academe and industry, many current problems will continue and could have an adverse effect on both the Canadian and US economies and on the employment prospects of IT workers (especially new entrants and older workers).
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".