Bibliographic record
Abstract
Dementia and Alzheimer’s disease know no boundaries. While this much is known, there is little beyond the medicalization of onset to provide insights into individuals instantly marginalized by a diagnosis with no future. The role of objects and storytelling in supporting the well-being and engagement of those dealing with Alzheimer’s disease and related dementias (ADRD) has recently become an accepted strategy in non-medical interventions for the disease. Many care facilities, day programs, and associations providing support for ADRD offer reminiscence and story sharing as regular activities. Building on research undertaken to explore how objects can be used as memory cues to evoke a memory of a person, place, event, or artefact in an individual’s personal narrative, this paper makes a case for mobilizing object memoir to empower the voices of the cognitively disabled. It argues for respecting the individual storyteller, not for the person he or she once was or may become in the future, but as someone with a unique identity and an inherent value as she or he is in the present. Object memoir as a readily invoked activity not only adds to the self-worth and social efficacy of an individual with ADRD, but also fosters meaningful connection with family, friends, and other caregivers who may be experiencing the loss of their own stories as memories of a shared past fade or disappear.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".