PAPER: Making Sense of the Gender Gap in Reading on the PISA 2009
Why this work is in the frame
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Bibliographic record
Abstract
Educational large-scale assessments such as the Programme for International Student Assessment (PISA) consistently show a gender gap in reading that favors girls over boys. However, there is significant score variability within gender groups that cannot be ignored. Therefore, the gender gap may be misleading (Alloway, 2007; Alloway & Gilbert, 1997; White, 2007). When information from other variables such as socioeconomic status (SES) is taken into account the gender gap does not apply to all boys or all girls to the same degree (Alloway, 2007). Consequently, there is a need to determine which boys are doing poorly in reading and which boys are doing well, relative to girls. This requires the use of latent class modeling (LCM) to allow detection of latent classes in the data set. Although each latent class (LC) typically consists of individuals from diverse manifest groups, each LC member shares a common response profile to a set of items. LCM has typically been conducted on the entire data set for the purpose of determining the degree of overall heterogeneity in the data (e.g. Oliveri, Ercikan & Zumbo, 2013). However, it would be challenging to determine whether there are any subgroups of boys and girls that do better than other subgroups, for instance. Therefore we conducted LCM on the Canadian version of the PISA 2009 reading data for boys and girls separately to investigate how response pattern heterogeneity for boys differed from girls. Considerable heterogeneity within gender was detected that posed validity implications for the gender gap. Additional regression analyses indicated that regardless of gender, high performing LCs (compared to lower performing classes) were more likely to do well in reading, come from higher SES families, speak English at home, enjoy reading, and utilize effective reading strategies. Implications of the results and future research directions are discussed. References Alloway, N. (2007). Swimming against the tide: Boys, literacies, and schooling – An Australian story. Canadian Journal of Education, 30 , 582-605. Alloway, N. & Gilbert, P. (1997). Boys and literacy: Lessons from Australia. Gender and Education, 9 , 49-60. Oliveri, M.E., Ercikan, K., & Zumbo, B. (2013). Analysis of sources of latent class differential item functioning in international assessments. International Journal of Testing, 13 ,272-293. White, B. (2007). Are girls better readers than boys? Which boys? Which girls? Canadian Journal of Education , 30, 554-581.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it