e-Skills: The International dimension and the Impact ofGlobalisation - Final Report 2014
Bibliographic record
Abstract
In today’s increasingly knowledge-based economies, new information and communication technologies are a key engine for growth fuelled by the innovative ideas of highly - skilled workers. However, obtaining adequate quantities of employees with the necessary e-skills is a challenge. This is a growing international problem with many countries having an insufficient numbers of workers with the right e-Skills. For example: Australia: “Even though there’s 10,000 jobs a year created in IT, there are only 4500 students studying IT at university, and not all of them graduate” (Talevski and Osman, 2013). Brazil: “Brazil’s ICT sector requires about 78,000 [new] people by 2014. But, according to Brasscom, there are only 33,000 youths studying ICT related courses in the country” (Ammachchi, 2012). Canada: “It is widely acknowledged that it is becoming inc reasingly difficult to recruit for a variety of critical ICT occupations –from entry level to seasoned” (Ticoll and Nordicity, 2012). Europe: It is estimated that there will be an e-skills gap within Europe of up to 900,000 (main forecast scenario) ICT pr actitioners by 2020” (Empirica, 2014). Japan: It is reported that 80% of IT and user companies report an e-skills shortage (IPA, IT HR White Paper, 2013) United States: “Unlike the fiscal cliff where we are still peering over the edge, we careened over the “IT Skills Cliff” some years ago as our economy digitalized, mobilized and further “technologized”, and our IT skilled labour supply failed to keep up” (Miano, 2013).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.064 | 0.014 |
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".