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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 sur OpenAlexaboutno aff
Nancy Hoffman

Notice bibliographique

RevuePhi Delta Kappan · 2011
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEducation Systems and Policy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWorkforceUnemploymentImmigrationYouth unemploymentPopulationPolitical scienceEconomic growthDemographic economicsSociologyEconomicsDemographyLaw
DOInon disponible

Résumé

récupéré en direct d'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. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,026
Score d'incertitude au seuil0,052

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0120,011
Communication savante0,0150,017
Science ouverte0,0010,009
Intégrité de la recherche0,0030,009
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,117
Tête enseignante GPT0,366
Écart entre enseignants0,249 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2011
Routes d'admission1
Résumé présentoui

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