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Record W2981152630 · doi:10.1177/0261429419878710

Learning from the voices and life trajectories of our most able students: A listening guide analysis

2019· article· en· W2981152630 on OpenAlexafffund
Harry Killas, C. Owen Lo, Marion Porath, Yuen Sze Michelle Tan, Chia‐Yen Hsieh, Rachel Ralph

Bibliographic record

VenueGifted Education International · 2019
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsUniversity of British ColumbiaEmily Carr University of Art and Design
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsActive listeningMeaning (existential)PsychologyVariety (cybernetics)PedagogyQualitative researchFoundation (evidence)SociologySocial scienceCommunicationHistory

Abstract

fetched live from OpenAlex

The “Superkids,” a group of highly gifted students, were first portrayed in a 2004 documentary. In response to the question of what happened to these students after the original film, a second documentary has been produced. The sequel focused on these individual’s lives, their retrospective insights about gifted education, their educational and career choices, and their reflections on their early adulthood. Transcripts of filmed interviews were analyzed using The Listening Guide, a qualitative method for understanding and interpreting voices. The researchers further highlighted first-person voices that may not have been apparent in interviews. This information was used to identify contrapuntal voices among the participants that reflected their views on the meaning of giftedness and their experience of studying in full-time congregated gifted programs. These voices provided a foundation for understanding the variety of pathways to accomplishment, the meaning of the gifted label, and the purpose of education at large.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0050.004
Scholarly communication0.0050.003
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.362
Teacher spread0.346 · 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 designQualitative
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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

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