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Record W4385867756 · doi:10.59962/9780774850513-017

Youth Employment Programs in British Columbia: Taking the High Road or the Low Road?

2007· book-chapter· en· W4385867756 on OpenAlexaboutno aff
Linda Wong, Stephen McBride

Bibliographic record

VenueUniversity of British Columbia Press eBooks · 2007
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
Fundersnot available
KeywordsRoad constructionTransport engineeringRoad transportRoad trafficRoad mapPolitical scienceGeographyEngineeringCartography

Abstract

fetched live from OpenAlex

Contemporary labour markets are characterized by heightened insecurity and the distinction between good and bad jobs.As a result of this, the transition from youth to adulthood has grown more difWcult.Labour market conditions for young people in Canada are poor on a variety of indicators.Rhetoric about lifelong learning and the extension of education hides the fact that many experience difWculties and delayed independence because of poor labour market conditions.Entry into the labour force is delayed, and this postpones the age of independent living.The fact that more young people are continuing their education represents a struggle for positional advantage within a difWcult environment, and it is as much an indicator of lack of adequate jobs as it is of a desire to invest in their own human capital resources.Human capital theory holds that the knowledge and skills possessed by individuals is a factor of production in the same way as is physical capital (buildings, raw materials, machinery, etc.).Higher levels of skill and knowledge, achieved through education and training, lead to higher productivity, which is expressed in higher earnings for those who possess them. 1 To the extent that labour market problems are accompanied by higher education enrolments, this theory enables policy makers to take a benign view of developments.A high skill economy, the result of major investments in human capital, is often referred to as a high road strategy -one that should be followed in preference to a low road strategy, which is based on low wages and leads to a race to the bottom in terms of labour and social standards.The high road strategy is portrayed as resulting in a virtuous circle of high skills, high productivity, and high wages.In general, this chapter concludes that British Columbia has failed to develop a "high road strategy" -a training system geared to a high skill economy.This failure has led, in turn, to a pursuit of policies that re-enforce the inequities of a neoliberal labour market, such as high unemployment, underemployment, greater use of contingent labour, and so on, rather than striving to overcome them. Youth Employment Programs in British Columbia 231 Labour Market Conditions for Canadian YouthWe start with an examination of labour market conditions for Canadian youth.While labour market conditions for adults remained poor during the 1990s, the situation for Canadian youth was worse.Unlike after the recession of the early 1980s, after the recession of the early 1990s the labour market for youth aged Wfteen to twenty-four was very slow to improve.Teenagers were particularly affected.Through much of the 1990s the youth unemployment rate was about 1.8 times higher than was that of adults.During late 1996 and 1997 the adult unemployment rate dropped, while the youth rate continued to increase.As a result, the youth-to-adult unemployment rate ratio went above 2.00, and it remained around 2.2 in the late 1990s. 2 The same bleak picture holds for earnings.Between the early 1980s and the early 1990s, the average earnings of youths fell by about 20 percent.3 Young people make up a high proportion of those earning the minimum wage and a disproportionate share of the part-time workforce.In total, the youth population constituted 58 percent of those making minimum wage.4 In 1970, 29 percent of young earners worked full year, full time; however, by 1995 this proportion had declined to only 16 percent.5 Participation rates among youth also decreased substantially during the 1990s.The adult participation rate declined moderately from 85.1 percent in 1990 to a low of 83.7 percent in 2000, compared to the drastic drop from 73 percent for youth in 1990 to 61.6 percent in 2000.6 While the adult unemployment rate was on a pattern of slow decline, youth unemployment increased until it hit a decade high of 17.3 percent in 1998.Since then there has been a modest decline, but it has yet to reach the 1990 level of 12.8 percent.7 While demographic factors may explain some of the decline in youth participation in British Columbia, there are indications that structural barriers and challenges in the labour market continue to challenge a young person's ability to Wnd work.For example, the number of Wfteento nineteen-year-olds not in school, not employed, and not actively looking for work increased from 12.6 percent in 1990 to 20.7 percent in 2000.8 One explanation of this is that young people in the 1990s showed a greater preference for enrolment in education and training programs.It is reasonable to suppose, however, that this "preference" was driven by a deteriorating labour market.Underemployment, in the sense of individuals working fewer hours than they would like, makes young people's situation even more difWcult.9 In the Wrst quarter of 1997 the underemployment rate for youth was 4.7 percent, double that of adult women and nearly three times that for adult men. 10 In 1996 the involuntary part-time rate among youth aged Wfteen to twenty-four was 10 percent, twice the rate of those over twenty-four.11 Canadian youth see themselves as "occupationally challenged," and they * Unless otherwise indicated the information came from interviews with participants working in the Youth Programs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.080
GPT teacher head0.205
Teacher spread0.125 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations1
Published2007
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Has abstractyes

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