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Record W4312964744 · doi:10.51952/9781447352570.bm001

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2021· paratext· en· W4312964744 on OpenAlexaboutno aff

Bibliographic record

VenuePolicy Press eBooks · 2021
Typeparatext
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
Fundersnot available
KeywordsIndex (typography)MathematicsComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Page numbers in italics refer to tables and figures; 'n' after a page number indicates the endnote number.A accessibility 36, 249 as barrier to aging well 47, 48 infrastructure accessibility 18, 105 lack of 27, 36 MD users 31, 33-7 Nova Scotia 191-2 parking accessibility 34 recommendations 34-5, 36-7, 47, 77 AFC (Age-Friendly City, WHO) 4-9, 115, 133, 223 Age-Friendly City Framework 5, 89, 110 age-friendly domains and key factors 5 apolitical nature 7 budget and funding 8, 9 City of Toronto 8 critiques 6-7, 128 framework 5-6 government and 4, 5-6, 7, 249 history 4-5 Indigenous Canada 199, 201, 223-4, 229-30, 234-6 leadership 8-9 Ontario 7, 8, 223 Ontario: Finding the Right Fit… 223-4, 229-30 policy implementation 7 politics and 8 recommendations 9, 128 success 8-9 voluntary policy 7, 8 WHO Age-Friendly City status 6, 74, 113, 169 ageism 46, 77, 94, 111-12, 137, 253 anti-ageism 48, 102-3, 112 definition 102 as greatest challenge 254 LGBTQ community and frail older adults 94, 102-3, 250 rural Canada 146, 158, 169 women 184 agency 252 MD users 31, 37-8 rural Canada: women 177-8, 178, 184 aging 2 age-friendly planning 1, 193, 245 Canada 1, 3, 4, 247-8 environment and 27 aging in place aging in place, wellbeing, and mobility 115-17, 118 benefits of 208 CAIP (Calgary Aging in Place Co-Operative) 22, 74-5 housing 91-2, 151 Indigenous Canada 208-9, 216, 229 rural Canada 151, 182-3, 184 suburban Canada 86, 91-2, 115, 122, 128 urban Canada 19, 20, 249 aging well 2, 4, 46-7 accessibility and 47, 48 Indigenous Canada, challenges to aging well 198-200, 220, 229, 230-4, 251-2 Indigenous Canada, opportunities to aging well 200-1 rural Canada, challenges to aging well 143-5, 150-7, 166, 167-9, 172, 251 rural Canada, opportunities to aging well 143, 146-7, 251 suburban Canada, challenges to aging well 84-5, 250 suburban Canada, opportunities to aging well 85-7, 172 Toronto 48-9 urban Canada, challenges to aging well 17-20, 45, 46-7, 77, 249 urban Canada, opportunities to aging well 20-2, 45, 77 Alberta 16, 82, 142, 247 Alzheimer's disease 21, 206, 247

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.177
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.001
Scholarly communication0.0060.005
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.8230.774

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 head0.067
GPT teacher head0.382
Teacher spread0.315 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

Explore more

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