Toward a culture of reading: four perspectives
Bibliographic record
Abstract
Other Factors and Student AchievementPoverty, and other health problems (poor nutrition, low birth weight, substandard housing, high violence, and substance abuse) associated with poverty, can depress achievement (Kober, 2001).The College Board (1999) reported in 1990 African-American children were three times as likely to be raised in low-income families as compared to White and Asian children.Hispanic children were twice as likely to be impoverished.Although a portion of the achievement gap can be explained by socioeconomic background, when these differences are factored out there is still an achievement gap that must be due to other factors (Kober, 2001).Gender differences must also be considered when examining achievement.Although gender differences in mathematics achievement have been declining, there is still a gap in upper-level mathematics course enrollment by females resulting in fewer females choosing professions in math, science, and technology (Dick & Rallis, 1991).Women are underrepresented in the fields of mathematics, science, and engineering contributing to the significant gap in earning ability between males and females.And, although women make up 44% of the work force, they make up only 15 % of those working in the fields of math, science, and technology (National Science Foundation, 1989).Johnson (2000) examined factors influencing advanced math coursework and students" attitudes toward math and found peer relationships have different effects on female and male decisions regarding mathematics.To address the influences of SES and gender differences on achievement, the present study controlled for these factors when examining the effects of peer and parent encouragement on math scores. MethodsUsing the National Education Longitudinal Study: 1988/2000 (NELS) dataset, the present study uses General Linear Modeling (GLM) to examine the effects of peer influence on high school mathematics achievement.The study considers parental encouragement, socio-economic level, race, and sex to investigate differential effects.In addition, the study used a longitudinal data analysis to assess the long-term effects of peer and parent influence relationships.Specifically, the following research questions were addressed: Does peer encouragement/ discouragement to take algebra show a significant difference in mathematics achievement in the eighth, tenth and twelfth grades?How does the relationship vary across ethnicity when controlled for SES and gender?Does encouragement of peers and parents have a lasting effect on mathematics achievement (8 th through 12 th grades)?How does the relationship vary across ethnicity when controlled for SES and gender?This study uses three measures from the NELS survey, in which 1988 schools and students were selected using a two-stage stratified probability design with over 24,000 students in more than 1,000 schools sampled.This study performs both cross-sectional and longitudinal analyses using General Linear Modeling (GLM) and Hierarchical Linear Modeling (HLM).
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".