Equality of educational opportunity in British Columbia : a study of ethnicity and schooling
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".