Age relations in student, staff, and faculty partnerships
Bibliographic record
Abstract
Population aging and the promotion of lifelong learning in higher education are changing student demographics. The result is more older learners in post-secondary classrooms and extracurricular programming. To better understand age diversity in extracurricular programming, this study examined age-relations in the Student as Partners [SaP] movement in higher education. From a partnered approach aligned with SaP principles, we used a critical grounded theory methodology to propose a substantive theory of age relations in student, staff, and faculty partnerships. Our findings focus on the core category: from age silos to embracing age diversity. We examine participants’ assumptions about age and treatment of age as a social taboo and outline how the organization of age in academic institutions contributed to participants’ perceptions that greater age and higher stage means more experience. To overcome age differences, partners developed professional intergenerational relationships and fostered age diverse environments. We discuss these findings in relation to intergenerational education and how learning from and with people of diverse ages is important for addressing ageism and creating inclusive age-diverse educational environments.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".