Making Meaning Count: A Phenomenological Approach to Understanding Student Meaning-Making Processes and Academic Outcomes
Bibliographic record
Abstract
The income-gap between Canadian families has widened in recent years. Students from low-income households often start their educational careers behind their peers. This gap in educational attainment and advantage often follows them throughout the duration of their educational development (Davies and Guppy 2010). While these systemic inequalities continue to perpetuate social processes resulting in the limitations of student capabilities, this paper works towards establishing a phenomenological lens which may be used to mitigate the disparity in the academic performance of students from low-income households compared to those of their peers – in particular, the ways in which poverty impacts self-concept and, ensuingly, academic performance amongst students. To establish this framework, this paper explores the phenomenological concepts of the life-world and the theory of embodiment. References: Davies, S., & Guppy, N. (2010). The schooled society: An introduction to the sociology of education. Oxford University Press. 198 Madison Avenue, New York, NY 10016
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".