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Record W843876540

Keeping Youths in School: An International Perspective: Blending Work and Learning May Provide Pathways That Ensure That More Students Are Able to Complete High School and Successfully Enter the Workforce

2011· article· en· W843876540 on OpenAlexaboutno aff
Nancy Hoffman

Bibliographic record

VenuePhi Delta Kappan · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceUnemploymentImmigrationYouth unemploymentPopulationPolitical scienceEconomic growthDemographic economicsSociologyEconomicsDemographyLaw
DOInot available

Abstract

fetched live from OpenAlex

The United States is not alone in confronting the challenge and frustration of not being able to ensure that every student completes high school. All of the countries belonging to the Organisation for Economic Co-operation and Development (OECD) have a group of left behind. These are the young people who don't complete upper secondary. Often, they're members of immigrant or minority groups, or they live in rural areas. However, there are important differences in the way the U.S. tackles the dropout challenge and what occurs in other OECD nations. Perhaps, in learning more about how other nations address the issue, the United States can discover ideas that would also work here. According to Jobs for Youth, a 16-country OECD study of transitions from school to employment, three-fourths of young left-behinds were already far removed from the labor market, either because they had been unemployed for more than a year or because they didn't seek a job. In the United States, this is a large group because of the sheer size of the population, and because the U.S. youth cohort is declining in numbers more slowly than in most European countries. During the current recession, this group will account for much of the rising youth unemployment, and it will grow as more youths experience longer periods of unemployment after leaving education (OECD 2009). The 16 OECD Jobs for Youth studies, including one on the United States, can be found at www.oecd.org/employment/youth. [ILLUSTRATION OMITTED] In winter 2009, at an international workshop at the OECD offices in Paris, a number of countries presented their approaches to stemming their dropout rates. The Netherlands, with a low but still worrisome noncompletion rate of 11%, described a comprehensive campaign to recapture dropouts--literally: A bus picks them up from the streets of Amsterdam and takes them to their programs. For struggling adolescents in danger of not completing upper secondary school, Norway (12%) shortened the vocational education structure from three or four years to a two-year integrated work and learning program. Better-performing countries structure combinations of work and learning to address specifically the needs of struggling young people. Even Korea, which has a high secondary completion rate (above 90%) and has a higher education completion rate that is second among the OECD countries only to Canada, asked for help with its small dropout problem. Korea is actually attempting to discourage so many young people from going on to postsecondary education, instead touting the virtues of strong vocational and technical high school programs. The analyses of why students drop out are remarkably similar across countries, but there are dramatic differences among countries in rates of dropping out and in solutions. Even definitions of dropout are a challenge: Some countries count as dropouts young people who don't complete a school-leaving certificate, others focus on a group labeled NEET--neither in education nor employment or training. Caution is required in comparing U.S. high schools with upper secondary schools. In many OECD countries, compulsory schooling ends at age 14 or 15; upper secondary schools are separate institutions serving 16- to 19-year-olds. The completion of these vocational programs is more like earning an associate's degree than a high school diploma, and their academic programs are more like one year of college. One way to avoid the problem of definitions is simply to ask which countries have kept the highest percentages of young people in school and transitioned them most successfully from schooling to work. The United States had a youth unemployment rate in 2008 of about 11%, while the OECD average was 14.4%. By July 2010, the U.S. rate had risen to about 19.1%, and it is continuing to rise. During that year, Australia, Austria, Canada, Denmark, Germany, Japan, Korea, the Netherlands, Norway, and Switzerland (lowest at 4. …

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)0.006
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0120.011
Scholarly communication0.0150.017
Open science0.0010.009
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0070.001

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 head0.117
GPT teacher head0.366
Teacher spread0.249 · 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
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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