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Prospects for Improving Cognition Throughout the Life Course

2008· editorial· en· W2128766063 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGothic.net · 2008
Typeeditorial
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsLife expectancyCognitionContext (archaeology)Life course approachPsychologyProductivityCognitive declineCognitive skillGerontologyDemographic economicsExpectancy theoryDemographyDevelopmental psychologyMedicineEconomic growthEconomicsSocial psychologyDementiaGeographySociologyPopulationPsychiatry

Abstract

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Life expectancy is at an all-time high and is likely to continue to improve rapidly in the future (Wang & Preston, 2009); this, coupled with a modest birth rate, means that the proportion of older adults will continue to grow in the United States, with the strongest growth occurring in the number of ‘‘oldest old’’—those over the age of 85. Absent dramatically higher levels of immigration and higher rates of productivity growth, it is likely that all of us will either be consuming far less before and after retirement or working much longer than we might have expected. The current economic crisis has resulted in huge losses in financial assets including 401(k) retirement accounts; older workers close to retirement may choose to work much longer than they expected, while some of those already retired may try to return to the labor force. In this context, it has become imperative for us to preserve or enhance cognitive functioning among older adults and to compress the duration of any cognitive decline. But what can be done to prevent and remediate agerelated declines in cognition? Given the central role that cognition plays in determining an individual’s independence and well-being, this becomes a very serious question for research. Hertzog, Kramer, Wilson, and Lindenberger (2008, this issue) present what we believe is the most comprehensive review to date of the science of cognitive improvement in aging and present a clear picture of the barriers to progress in this area. Although they take a clear stand on the question of whether it is possible to remediate age-related cognitive decline (for the impatient, their answer is: Yes we can!), those holding opposing points of view will also find much value in this monograph. The National Institute on Aging (NIA) considers this topic to be one of paramount importance. In 2007, the NIA and the McKnight Brain Research Foundation cosponsored a Cognitive Aging Summit that prominently featured animated discussion of cognitive enhancement in aging (see http://www.health.ufl.edu/ brain/summit/index.htm for meeting materials). NIA’s research focus on enhancement spans many levels, from genes to cells to neural circuits to systems and on up through social engagement and societies. Hertzog and colleagues cover many of these levels in some detail, so we will only point out some selected areas that received less attention here and that could have important implications for the public interest and future research.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.040
GPT teacher head0.405
Teacher spread0.365 · 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