Dancing With Our Partners: An Exploration of Story and Resonance in the Literacy Environment
Bibliographic record
Abstract
This thesis describes a study that was done with tutors and students in Frontier College’s Beat the Street: Literacy and Basic Skills program. Using a qualitative methodology, it focuses on stories of literacy, life and learning from tutors and students. The author’s own experiences, stories and reflections as a tutor are an important piece of the work. The thesis operates on and argues for the notion that people are made up of their stories, and that they interact with other people and the world through those stories. This research process revealed many ways in which tutor and student perceptions of literacy, learning, and each other were affected by their stories. It also revealed that in the overlaps between stories lies the potential for a moment of profound connection and learning the author describes as resonance. The thesis explores some of the ways resonance was perceived to enhance the literacy environment.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".