Equity, literacy testing, and marginalized youth: the social construction of "illiterate" identities
Bibliographic record
Abstract
This study explores how the large-scale, high-stakes, standardized Ontario Secondary School Literacy Test (OSSLT) produced by the Education Quality and Accountability Office (EQAO) affects Ontario secondary students who have failed it. I strategically use the poststructuralist work of Lesko and Foucault, and incorporate the work of feminist, critical policy and anti-oppressive educational theorists (Arendt, Lipman, Thomson, TeRiele, Walkerdine) to look at the ways in which those who find themselves marginalized by test failure resist and/or are constrained by their "illiterate identities." I draw upon the experiences of youth and teachers to reflect on policy, literacy and testing. The methodology of the study is based on in-depth, semi-structured, qualitative interviews conducted with teachers and youth. The youth participants have failed the OSSLT once or twice, and some are completing the literacy course in order to fulfill their high school graduation requirement. This study draws out implications for education policy. EQAO claims its work is conducted in the best interest of students to promote excellence in 'learning and teaching,' and 'equity'. Through a juxtaposition of policy documents and personal narratives, I show the discrepancy between the policy and the lived experience of youth and teachers. I further examine how youth and teachers' identities and knowledge are positioned by this policy discourse. Overall, I argue that high-stakes standardized literacy testing in Ontario continues to build upon the legacy of colonialism, masculine domination, and policies that sought to produce 'good citizens' through discipline and 'proper' development. These oppressive mechanisms are seen in the state's power to sort and select students and produce deficient identities. The "illiterate" student and/or "at risk" youth revealed by the test acts as one more form of social control projected upon youth who have historically been deemed "other." Given the historical forces that govern the discourse of youth, their status as a literate or illiterate subject within the state functions as an attribute that renders some youth superior and others inferior. I conclude the thesis by examining the ethical and political implications of this study for policy makers, literacy curriculum, and theorizing on youth, identity and equity in education. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".