How to sustain a good life with dementia?
Bibliographic record
Abstract
… an instruction is always a story cut too short. Sravansky and Stengers, ‘Relearning the art of paying attention: A conversation’, 2018 FIELD NOTES September 2015 Wednesday is one of Marla's regular day programme days. Ken tells me that he has been unable to use DATS [Disability Action Transport Scheme] (the transport provided) because it is difficult to ensure that Marla will be ready to go on time – getting up and dressed is a struggle some days, and if Marla isn't ready, it's a problem. Plus, he was never sure exactly what time the van would arrive, and having her dressed for outside and waiting was an issue. I am going with them to the day programme because Ken thinks it is important for me to see what they do there and how important it is for him that she goes there (she now attends three days/week). I arrive at the house at 8:40 – Ken has asked me to arrive early because he wants me to ‘see’ what getting ready to go out is like. I arrive, the house is very tidy, breakfast dishes have been washed and put away, radio is playing music, both Ken and Marla are dressed. Marla is wandering around the house.[…] He finds Marla and says ‘Let's go do your hair’ and takes her into the bathroom. She resists his brushing her hair but then they decide she looks nice. He then helps her to brush her teeth and is concerned when she swallows the toothpaste; he tries to get her to rinse her mouth and admonishes her about the toothpaste. Then he tells her she ‘looks good, really sharp’. He seems to go back and forth between gentleness and sharpness.[…] Getting Marla into her outside jacket is a struggle. Ken tries to reason with her ‘I just put it on, why take it off?’ It's a chore to have her sit long enough to put her shoes on. Ken goes to get himself ready to leave and I walk around the house with Marla. She says ‘Ken is always fussing’. In this excerpt Ken is showing a “normal” day, as if to ensure that it is the practicalities of ‘doing dementia’ at home that become part of their story.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".