Bibliographic record
Abstract
As humans age, physical, perceptual and cognitive abilities deteriorate outright or fail gradually, thus affecting the qual-ity of life. We offer a third year engineering product design course where the project theme over the past three years has been disabling conditions for the elderly (i.e., healthy ag-ing). The technology-pull theme exposes the students to the process of designing cost effective assistive technology; and shows the important role engineering design has in society to improve the quality of life for all. The methodology adapted for the course is for the project groups to be in constant in-teraction with the potential user of the resulting technology. The students are required to interact with at least 2 people with the disabling condition during each of the design phases. The project requires for the students to understand the dis-abling condition, choose a relevant problem associated with the condition, identify the customer needs and map this into product requirements and come up with concepts and pro-totypes in function and form of a concept that best satisfies the needs. The design phase is broken into 4 or 5 phases where the instructor, teaching assistants and fellow peers re-view each stage with a class critique. The final results are presented at an exhibit that is open to the public and the users are also invited to attend. The solutions range from the sim-plistic to the complicated (i.e., requiring AI). What we argue is that this design process is essential before addressing the need for AI for eldercare. There may be a need for AI and there may not be. We illustrate this by selecting four projects from the 2007 course offering, which addressed problems as-sociated with Alzheimer;’s, Parkinson’s, falls by people with walkers and independent living for the elderly at home.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".