MétaCan
Menu
Back to cohort
Record W2960122422 · doi:10.1080/14636778.2019.1637722

The turn towards prevention – moral narratives and the vascularization of Alzheimer’s disease

2019· article· en· W2960122422 on OpenAlexafffund
Annette Leibing

Bibliographic record

VenueNew Genetics and Society · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsDementiaFallacyDiseaseValue (mathematics)PsychologyPublic healthVascular dementiaMedicineEpistemologyPathology

Abstract

fetched live from OpenAlex

Recently, a major turning point in the field of dementia research has occurred: serious and independent studies have shown that there are risk factors that can be modified in ways that reduce dementia risk. However, the reality behind this hopeful message is much more complex than simply translating it into concrete public health prescriptions regarding medications and lifestyle changes. The objective of this article is twofold: 1. In line with observations from other authors in the field of critical public health, the recent turn in dementia epistemologies will be described as a fallacy when conceived as individualized behavioral recommendations; 2. The profound change in the notion of risk for Alzheimer’s disease (AD) – now defined using nearly identical risk factors as Vascular dementia (“vascularization of AD”) – has also had an important impact on whether people are classified as “good citizens.” After a short description of recent studies about Alzheimer’s disease and the new risk factors associated with it, three interrelated historical changes are described as being constitutive of the “new dementia.” Based on fieldwork in geriatrics in Brazil, one example will be given that illustrates how recent changes in understanding dementia result in value-laden models which, as a consequence, are sorting out people.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

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

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 head0.031
GPT teacher head0.267
Teacher spread0.236 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations12
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueNew Genetics and SocietySame topicMental Health and PsychiatryFrench-language works237,207