MétaCan
Menu
Back to cohort
Record W4280507375 · doi:10.4025/jphyseduc.v33i1.3312

The two sides of sedentary behavior

2022· article· en· W4280507375 on OpenAlexaff
Bruno Gonçalves Galdino da Costa, Jean‐Philippe Chaput, Kelly Samara da Silva

Bibliographic record

VenueJournal of Physical Education · 2022
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsChildren's Hospital of Eastern Ontario
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsTypologyFace (sociological concept)PsychologyField (mathematics)PopulationScientific evidencePublic relationsPolitical scienceSociologySocial scienceEpistemology

Abstract

fetched live from OpenAlex

Sedentary behavior (SB) has become a prevalent behavior amongst several population subgroups worldwide. This increase in SB is alarming, as this behavior has been associated with several adverse health outcomes. With the advancement of technology, the relationship of individuals with SB has become increasingly complex, and available instruments, theories, and research face challenges to keep up with this evolution. Four issues regarding research on SB are discussed in this opinion article: (i) advances in its typology and measure; (ii) health impact of quantitative and qualitative indicators of SB; (iii) the good side of SB; and (iv) challenges and future directions of studies in this field of knowledge. This opinion article raises some questions based on the limitations of current research with its advances and gaps. Some challenges and research recommendations are compiled, and other can be drawn from the ever-growing scientific evidence related to SB across different fields.

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)0.012
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.009
Scholarly communication0.0060.006
Open science0.0010.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.031
GPT teacher head0.401
Teacher spread0.370 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physical EducationSame topicPhysical Activity and HealthFrench-language works237,207