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Record W4386340066 · doi:10.1515/9781800734982

Preventing Dementia?

2020· book· en· W4386340066 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBerghahn Books · 2020
Typebook
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchEidgenössische Technische Hochschule ZürichBundesministerium für GesundheitNational Institute on AgingSocial Sciences and Humanities Research Council of CanadaDeutsche ForschungsgemeinschaftJohns Hopkins UniversityPrinceton University
KeywordsDementiaComputer scienceMedicineInternal medicineDisease

Abstract

fetched live from OpenAlex

The conceptualization of dementia has changed dramatically in recent years with the claim that, through early detection and by controlling several risk factors, a prevention of dementia is possible. Although encouraging and providing hope against this feared condition, this claim is open to scrutiny. This volume looks at how this new conceptualization ignores many of the factors which influence a dementia sufferers’ prognosis, including their history with education, food and exercise as well as their living in different epistemic cultures. The central aim is to question the concept of prevention and analyze its impact on aging people and aging societies.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.056
GPT teacher head0.373
Teacher spread0.316 · 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