Where to Start the Journey to Advance Age Inclusivity at Your Institution
Bibliographic record
Abstract
Abstract Each institution’s journey to becoming more age inclusive will to depend on its unique characteristics, and be dependent on its strengths and existing gaps. A good place to start is to explore how to build connections and leverage existing initiatives, such as research programs, community connections and importantly the institution’s strategic plan. At this point, elements to consider include coalition building, identifying strengths and gaps, and reframing aging. Because ageism can be a hindrance in many ways, strategies to address ageism should be included. GSA initiatives and tools such as the Reframing Aging Initiative, Ageism First Aid and AARPs Disrupt Aging will be highlighted in our presentation. Examples of how several universities have charted their course to becoming more age-inclusive and age-friendly will be outlined.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.074 | 0.029 |
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".