Equality of educational opportunity in British Columbia : a study of ethnicity and schooling
Notice bibliographique
Résumé
The purpose of this study was to determine the relationships among ethnicity and equality of educational opportunity according to access and treatment of students while considering outcomes in the school districts of British Columbia. Two demographic variables, ethnic composition and student population, were used as well as indicators of access, treatment and outcome. The population of British Columbia was initially divided into two ethnic categories: English and Non-English. The Non-English category was then subdivided into four categories: French, Aboriginal, Later Europeans and later Visible Minorities or Afro-Asians. Access indicators were represented by school resources as they are associated with teacher qualifications and experience, student/teacher ratio at the elementary and secondary level and dollar expenditure for instructional resources. Treatment indicators were according to the nature of the special education and English as a Second Language programmes. In particular, the indicators used for treatment were the percentage of students in the programmes, the total dollar expenditure on the programmes, the student/teacher ratio in the programmes, the number of students approved by the Ministry of Education for English as a Second Language and the dollar expenditure on special education materials. Finally, the outcome indicators used were the mean achievement levels of students in school districts for Reading, Mathematics and Science at Grades four, eight and twelve plus the percentage of graduates in each school district. A theoretical research model was developed to indicate the relationships and the weak causal links between the two demographic variables, the access indicators, the treatment indicators and the outcome indicators. The statistical analysis had four distinct phases which tested the theoretical research model and the relationships among and between the indicators. Descriptive analyses of raw data represented the first stage of statistical analysis. The second phase was a correlational analysis of the relationships among the indicators. Factor analysis, the third phase, produced five underlying factors. The demographic variables, ethnicity and student population, remained unfactored while the access indicators had two underlying constructs, teacher characteristics and student/teacher ratios. The treatment indicators also yielded two factors, special education and English as a Second Language. Of the outcome indicators, only the achievement score variables were factor analyzed and these yielded a one factor solution. Therefore, the achievement factor and the percentage of graduates represented the outcomes. These factors and other indicators were then tested in the theoretical model by the fourth step of statistical treatment, the path analysis. This technique was used to evaluate the theorized causal relationship among demographic variables (ethnicity and enrolment), access factors (teacher characteristics and student/teacher ratios), treatment factors (special education and English as a Second Language), one outcome factor (achievement), and an outcome variable (percentage of Grade 12 graduates). The procedures outlined above yielded three main conclusions. First, certain ethnic groups have differential access to the educational resources of student/teacher ratio and teacher characteristics. The second main conclusion was that ethnic groups do have different outcomes. Finally, the study found no relationship among the special treatment variables and the outcome measures. The reader is directed to the section on limitations of study (pp. 167 - 171) where limitations are comprehensively discussed. It is important to be reminded that the analyses being made with the school district as the unit of analysis restricts the generalizations that can arise from this investigation. It also recognizes that this study has focussed neither on the process of school nor on ethnic family patterns and life styles or individual learning styles.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».