MétaCan
Menu
Back to cohort

Reexamining the boundaries of the ‘normal’ in ageing

2011· article· en· W2165939261 on OpenAlexaff
Hannah M. O’Rourke, Christine Ceci

Bibliographic record

VenueNursing Inquiry · 2011
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAgeingContext (archaeology)Cognitive declineActive ageingCognitionNatural (archaeology)PsychologyDementiaSociologyOlder peopleGerontologyEpistemologyDevelopmental psychologyDiseaseMedicineHistoryNeurosciencePhilosophy

Abstract

fetched live from OpenAlex

Textbooks and policy documents tend to present the boundary between normal and abnormal ageing as natural and clearly demarcated. In this study, we trouble the notion of natural and clearly demarcated boundaries between normal and abnormal ageing by considering how these boundaries have been established and maintained in present-day Western contexts. We draw on both Canguilhem's discussion of the normal and the abnormal and Foucault's emphasis on the role of the sociohistorical context in the social practice of boundary generation. In doing so, we critically examine common conceptualizations of normal and abnormal ageing, including those found in antiageing science, successful ageing and healthy ageing policy discourses and in health education textbooks. We argue that the growing emphasis on 'healthy' ageing both reflects and shapes the societal views of those individuals who are not able to remain disease-free and represents a kind of mystification of ageing where ageing without functional or cognitive decline is instituted as the norm. Awareness of the role that the social context plays in shaping definitions of normal and abnormal ageing encourages critical consideration of the effects that Western conceptualizations of normal ageing may have for older adults who continue to age with cognitive or functional decline.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.168
GPT teacher head0.396
Teacher spread0.228 · 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 designObservational
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

Citations14
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueNursing InquirySame topicAging and Gerontology ResearchFrench-language works237,207