MétaCan
Menu
← Back to cohort
Record W4401632978 · doi:10.22215/etd/2024-16032

Forget Me Not: Defending the Right to Place for People Experiencing Dementia and Ageing in the Newtonbrook West Neighbourhood in Toronto

2024· dissertation· en· W4401632978 on OpenAlexaffabout
Caitlin Samantha Chin

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsDementiaNeighbourhood (mathematics)InterdependencePsychological interventionPsychologySociologyMedicinePsychiatrySocial science

Abstract

fetched live from OpenAlex

How can we confront the ableist and ageist-built environment through the lens of dementia? Dementia is an umbrella term for the cognitive condition that affects memory, behavioral reactions, and daily tasks that can lead a person to feel isolated from the rest of the world. It is a condition that progressively worsens over time with no cure. For those with dementia and aging, it is the norm to send them to institutions, removing them from their familiar environment. What if the neighborhood could adapt so those with dementia did not have to be displaced? This thesis is grounded in my personal experience of a loved one, intergenerational memory, and examining the built world as barriers. Through speculative neighborhood interventions, a more inclusive neighborhood can be explored that embraces interdependent living while also protecting people’s rights to their community and 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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0300.007
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.011
GPT teacher head0.309
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same topicMigration, Aging, and Tourism Studies→French-language works237,207→