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Record W4396951109 · doi:10.1111/jora.12958

Embracing the hidden potential: The contribution of majority world research to developmental science

2024· article· en· W4396951109 on OpenAlexaff
Philip Baiden, Yao Zheng, Amanda J. Nguyen, Graciela Espinosa‐Hernández, Lucía Magis‐Weinberg, Peter F. Titzmann

Bibliographic record

VenueJournal of Research on Adolescence · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDevelopmental ScienceEmic and eticPsychologyFace (sociological concept)Representation (politics)Field (mathematics)Inclusion (mineral)Cognitive scienceDevelopmental psychologySocial scienceSociologySocial psychologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Research on adolescence from the Majority World possesses major hidden potential in contributing to global adolescent research and developmental science more broadly. In this commentary, the authors (1) describe the background and the process through which this special issue came into fruition, (2) introduce the emic approaches to study the influences of macro-contextual variations on developmental science and provide several pertinent examples on the contributions of Majority World research, (3) elaborate on challenges and barriers that Majority World scholars often face in conducting and disseminating their research, and (4) a few actionable steps and recommendations in promoting the representation and inclusion of Majority World research into global developmental science. Only when our field fully integrates findings from all regions of the world will we be able to develop a fundamental scientific representation and understanding of what it means to be an adolescent, how adolescents develop over time, and what tasks or phenomena in adolescent development are truly universal or specific to particular groups, regions, or areas.

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)0.044
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
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 score0.588
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0020.002
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.104
GPT teacher head0.483
Teacher spread0.379 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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