MétaCan
Menu
Retour à la cohorte
Enregistrement W384774588

The Great Unknown: Does the Black-White Test-Score Gap Narrow or Widen through the School Years? It Depends on How You Measure. (Check the Facts)

2003· article· en· W384774588 sur OpenAlexaboutno aff
Jens Ludwig

Notice bibliographique

RevueEducation next · 2003
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEducation Systems and Policy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWhite (mutation)Test (biology)Quarter (Canadian coin)Statistics educationTest scorePsychologyDemographic economicsStandardized testPolitical scienceEconomicsMathematics educationHistory
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Educational Achievement and Black-White Inequality By Jonathan Jacobsen, Cara Olsen, Jennifer Kinq Rice, Stephen Sweetland, and John Ralph National Center for Education Statistics, July 2001. Through the 1960s, African-Americans earned much less than whites--even when their cognitive abilities (as measured by test scores) were similar. By the end of the century, however, many believed that employment discrimination had attenuated to such a degree that the gap in labor-market outcomes could be explained almost entirely by differences in test scores. Consequently, reducing the well-known gap between the test scores of black and white students is now seen as an important way to reduce economic and other forms of inequality. In response, the U.S. Department of Education's National Center for Education Statistics commissioned Mathemarica Policy Research, a contract research firm, to find out whether 1) employers are now playing fair and 2) schools are doing their part in narrowing the black-white testscore gap. Accepting the challenge, Jonathan Jacobsen and his colleagues discovered that the answer to the first of these questions was both more hopeful and much easier to provide. Few dispute the dreadful state of affairs that existed in the 1960s. One study found that the black-white gap in scores on the Armed Forces Qualifying Test in 1964 could account for only a quarter of the difference in wages between black men and white men. Quite clearly, widespread racial discrimination helped create a labor market that yielded fewer rewards for blacks than for whites of similar ability. In addition, many jobs did not require a high level of cognitive skill. Today, cognitive skills and educational credentials are more valuable for workers in part because of changes in production technologies that demand more highly skilled employees. Moreover, the civil-rights legislation of the 1960s and 1970s seems to have caused discrimination in employment to decline. As a result, the black-white gap in academic achievement now seems to account for a sizable share of the black-white gap in wages. Whether the gap in labor-market outcomes has virtually disappeared depends on how it is measured. In a widely cited 1996 study, economists Derek Neal and William Johnson showed that if test scores are not taken into account, white men's wages are 24 to 28 percent higher than those of black men. But these raw differences narrow substantially when differences in test scores are accounted for. Neal and Johnson, using data from the National Longitudinal Survey of Youth begun in 1979, showed that white men earned hourly wages that are only 7 to 10 percent more than black men with similar scores on the Armed Forces Qualifying Test. However, the gap in annual earnings between white men and black men with similar scores was about three times as large (roughly 30 percent), This is because white men are less likely to be unemployed, work longer hours on average, and are on the job more days of the year. Among women, wages and earnings were actually somewhat higher for blacks than for whites with similar test scores. Confirmation Using data from a variety of sources, including the National Longitudinal Survey of Youth, the High School and Beyond study, and the National Longitudinal Study of the High School Class of 1972, Jacobsen and his colleagues at Mathematica essentially confirm Neal and Johnson's findings, providing additional evidence that most of the remaining wage gap is due to differences in cognitive skills, as measured by test scores. In the Jacobsen study, the hourly wages, employment rates, and annual earnings of black women were at least as high as those of white women with similar test scores and family backgrounds. The researchers also found that black and white men with similar scores and family backgrounds had similar wages and, contrary to the findings of Neal and Johnson, generally experienced similar employment rates and annual earnings as well. …

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)0,007
score de la tête « metaresearch » (Gemma)0,089
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,040
Score d'incertitude au seuil0,106

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,089
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,004
Études des sciences et des technologies0,0030,003
Communication savante0,0040,008
Science ouverte0,0020,002
Intégrité de la recherche0,0050,007
Charge utile insuffisante (le modèle a refusé de juger)0,0320,022

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 Opus0,088
Tête enseignante GPT0,355
Écart entre enseignants0,266 · 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'étudeObservationnel
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

Citations1
Publié2003
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueEducation nextMême sujetEducation Systems and PolicyTravaux en français237 207