Honors Work: Seeing Gaps, Combining Gifts, Focusing on Wider Human Needs
Bibliographic record
Abstract
“Honors Work: Seeing Gaps, Combining Gifts, Focusing on Wider Human Needs” describes the authors’ collaborative work with high school girls to bring Canadian activist Leigh Boyle and “The Lipstick Project” story to Maine in April, 2017. “The Lipstick Project,” which Boyle founded and directs, is a women-run volunteer organization based in Vancouver that provides free, professional spa care services to terminally ill patients. The authors contend that their collective efforts with the high school girls to organize “The Lipstick Project” events in Maine brought together a number of community constituencies in important ways, reflecting qualities and values central to honors education. The authors cite the writings of the late Samuel Schuman, a widely involved and highly respected honors administrator and teacher, for their characterization of honors education as, at its best, engaged, imaginative, and socially conscious. The authors note how, through Boyle’s visit and “The Lipstick Project” gatherings, they confronted significant and bridgeable gaps: gaps between high school girls and college women, gaps among care providers and the university community, gaps in understanding the need for creative care. They conclude that identifying and addressing notable gaps can be an excellent starting point for an honors undertaking, particularly gaps that cross disciplines, form links to the local community, and focus on broader humanist concerns. They offer their experience as a replicable model for other honors communities to consider.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.032 | 0.036 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".