Critical reflections from the millennials on the global action against dementia legacy events
Bibliographic record
Abstract
Purpose – The purpose of this paper is to share information regarding the Global Action Against Dementia Legacy, to critically reflect on the views of the Canadian Young Leaders of Dementia and to strengthen the impact of their voices in the global discussion surrounding dementia. Design/methodology/approach – This offers a critical reflection and review of the innovative intergenerational discussions and solutions offered by younger Canadians – specifically, the Millennial Generation. Findings – The paper provides insights about how change and solutions in dementia actions may be established through intergenerational collaboration. Research limitations/implications – Researchers are encouraged to make room for the voices of younger, less established generations in both discussions and research related to dementia. The younger generations will provide future direction to the Global Action Against Dementia Legacy so it is time to hear their voice too. Originality/value – This paper draws on developments in the Canadian context to highlight the potential of encouraging a less-usual, intergenerational approach to developing engagement, research and solutions in dementia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.046 | 0.048 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".