Unspoken Words Beyond the Numbers: Lived Experiences of Low Socioeconomic Status Post-Secondary Students
Bibliographic record
Abstract
Over the past few years, there has been a growing interest amongst education stakeholders and institutional organizations across Canada, to increase access to post-secondary education (PSE) for students from low-socioeconomic status (SES) backgrounds. More specifically, this study sought to understand these dynamics within an Ontario higher education context. Despite low-income students having some degree of access to PSE through the current and past provincial policies (e.g., domestic tuition freezes, slashing university tuition by 10%, access to the Ontario Student Assistant Program; OSAP), education stakeholders have failed to accommodate for these low-income students’ lived experiences (Usher, 2022). These experiences have been cited in the literature for having significant influences on low-SES student learning outcomes, and the overall student integrity and well-being throughout their PSE. Yet, there remains a grave disconnect between stakeholders, organizations, and policy makers, and the low-income students for whom these policies and practices are being designed and implemented. In essence, these entities of authority have forgotten the low-income student voices. By utilizing a phenomenological research approach, this study sought to bridge this gap, and capture the rich accounts of these participants lived experiences through one-on-one interviews. Additionally, this study also sought to understand how these lived experiences shaped these students’ learning outcomes throughout their first year of PSE. By utilizing Social Constructivism theory to inform the findings and construction of a working theory model, this study adds to the literature by exploring how these lived experiences were felt and operationalized as actions amongst these low-income participants. Stakeholders and policy makers concerned with educational persistence and outcomes, can use the findings and recommendations laid out in this study, to help low-income students across a variety of settings and circumstances.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".