From Computational Thinking to Political Resistance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Working with Latinx adolescents and providing informal learning experiences through a voluntary after school program aimed at developing their computational thinking and competencies, discussions around the election of Donald Trump emerged as it was important for many students. Emergent bilinguals internalized the xenophobic discourse that was amplified during the presidential election season. Thus, students feared deportation, discrimination, and other forms of violence. Many students experienced their friends and families facing deportation due to the increased Immigration and Customs Enforcement raids that occurred in 2016. The after-school program was a safe space in which we could provide a constructive and creative environment for students to comfortably explore these topics. In this paper, drawing on three pre-service teachers’ reflections, we explore how middle school urban Latinx students navigated their oppressors through computational thinking and show case a model of a critical pedagogy in an informal science education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it