Sentimental Objects, Meaningful Connections: \nDesigning a Narrative-Based Framework to Support the Passing of Significant Personal Belongings into New Hands.
Bibliographic record
Abstract
Canada’s population is aging: there are now more people in this country over the age of 65 than under the age of 15. This older population is looking to the future and seeking more freedom and less home maintenance; alternately, they are unable to stay in their homes due to changed physical, mental, or financial concerns, that entail moving out of necessity, even unwillingly. In the process, many baby boomers are downsizing, decluttering their existing homes where they raised their families and accumulated a lifetime of goods, including significant personal belongings that are markers of personal identity. This process of downsizing and purging leads to emotional roadblocks when these individuals are confronted with these sentimental, personal belongings: items that they would either like to pass on to the next generation or dispose of, but find doing so extremely difficult. This study examines this phenomenon both from the perspective of older persons who have recently downsized and from individuals of any age who have recently moved and have been faced with the reality of parting with significant belongings. \nThis study seeks to answer the questions: what is the connection between personal objects and memory? How might we use storytelling to access, preserve, and share these stored memories, ultimately working toward the development of a framework that would allow for these stories to be shared, ensuring that the object retains its value while passing it onto someone else, in a way that minimizes a sense of pain or loss on the part of the original owner?
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".