Global Migration as a Solution to Worker Shortages in Industrialized Economies
Bibliographic record
Abstract
Introduction According to a Manpower (2007) survey of 37,000 employers in twenty-seven countries and territories, 41 percent of employers worldwide are having difficulty filling positions due to suitable talent available in their market. top ten jobs needed globally include drivers, healthcare workers, laborers, machinists/machine operators, production operators, skilled manual trades, technicians, and others. In the United States, one of the largest growing employment shortages is in the healthcare field, especially in nursing. are several contributing factors, including the aging of the workforce, shortage of individuals entering the field, and unhappiness with workplace conditions (US General Accounting Office, 2001). These factors not only affect the healthcare field, but also can be applied to other manual-type labor positions as well. Canada immigration lists the construction and skilled trades, machining and equipment operators, automotive, engineering, electrical, industrial manufacturing, agriculture and healthcare workers as areas of confirmed labor market shortages (Canadian Citizenship and Immigration Resource Center Inc, 2007). Australia is experiencing a shortage in healthcare professionals and skilled labor, two of the occupations targeted for permanent migration (Janet Phillips, 2006). New Zealand has increased its immigration quotas for information technology, medicine, and professions requiring a master's or doctorate degree. (SHRM Global HR Focus Area, 2006) Business owners in the United Kingdom list a lack of availability of a skilled workforce as a major constraint to doing business (Grant Thornton International, 2006). These shortages are expected to increase over the next ten years. As the baby-boom generation retires, there are not enough skilled workers to replace the shortage. Occupational Outlook shows that The number of people in the labor force aged 65 and older is expected to increase more than three times as fast as the total labor force, due, in part, to workers postponing retirement. Compared with the total labor force, the number of workers younger than age 45 is expected to grow more slowly or to decline. (US Government 2003-2004). Even if the number of workers over 65 increases an unknown factor is the change in morbidity rate for this cohort. Aging also brings an increase in physical and mental ailments. Even if the intention of both workers and employers is to continue the work relationship, health issues may supersede planning efforts. As these skilled workers retire and/or die, the skills shortage will become even more acute as the future workforce is expected to be trained in skills other than traditional blue and pink-collar type positions. In the past, manual labor type jobs involved learning a basic skill set then doing a process according to a set of procedures that could be followed in most situations. Procedures were generally written by better-educated white collar workers. Advanced education was not necessarily required. For example, elementary school teachers taught the same types of basic skill sets to each new age cohort. Although occasionally a new teaching methodology emerged, the process remained generally the same throughout the twentieth century. latent functions of school--learning how to obey authority, consideration of others, etc.--fulfilled a basic societal need of an emerging industrialized economy. Machine operators, nurses, and other traditional working class positions operated in basically the same fashion. Procedures were designed by well-educated owners and managers, and then carried out routinely by the manual labor workforce. Today's workforce has been encouraged to shun these types of jobs. Manual labor is hard work for long hours at an average or below average wage. According to Thomas Friedman (252), this shunning of positions relates to classic wealthy family generations. There is something about post-World War II America that reminds me of the classic wealthy family that by the third generation starts to squander its wealth. …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".