Street Papers: An Educational Tool for Social Change
Bibliographic record
Abstract
Abstract \nStreet Papers: An Educational Tool for Social Change \nMaria Daniela Aranibar Zeballos \nDiscourses and representations of homelessness and poverty tend to emphasize individual factors, while disregarding systemic causes. However, public discourses are shifting, as local governments begin to recognize structural inequalities that propel individuals into poverty and take them into account in policymaking. Therefore, initiatives that reframe representations of homelessness, need to be properly examined. \nThis research project examines the processes through which public understandings of homelessness can be transformed by looking at one such initiative: street newspapers. With an urban qualitative methodological approach, this project assesses the educational value of Montreal’s local street paper, L’Itinéraire. I analyze the data analysis results using a theoretical lens based on social justice and public pedagogy frameworks. \nThis project addresses three research questions: \n(1) How does participation in street papers impact the sense of agency of those who have experienced homelessness or poverty, if at all? \n(2) Does reading street papers influence individuals perception of homelessness and poverty? \n(3) How might public actions/attitudes towards homelessness and poverty be impacted? \nMy results show that L’Itinéraire impacts the sense of agency of its vendors, and shifts readers’ perspectives on homelessness, poverty and other social issues by enabling them to critically interrogate common sources of knowledge production, by legitimizing marginalized voices, by starting processes of collective reflection on current social structures and by ultimately creating spaces for social action. However, vendors and readers alike feel their ability to enact social change is limited, especially at an individual level.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.138 | 0.023 |
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".