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Record W2940485671

Mythology of gifted: A case study on the impact of labeling on self-knowledge

2019· article· en· W2940485671 on OpenAlexaff
Megan Chrostowski, C. Owen Lo, I-Chen Wu, Yuen Sze Michelle Tan

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCognitive dissonancePsychologyMythologyGifted educationSocial psychologyMathematics educationPedagogy
DOInot available

Abstract

fetched live from OpenAlex

As there is debate over whether students within gifted education programs realize benefits from their educational labels (Berlin 2009, Moulton et al 1998), we aimed to explore the formation of self-knowledge in light of a gifted label and explore some positive social and academic adjustments contingent on this self-knowledge. Most of the participants with a single label, “gifted”, (1) spoke of a social mythology that led to pressure to perform and internal dissonance around “being gifted”. Of note, there was a marked difference in experience for those who had concomitant special learning needs (SLN) labels. Those students articulated a strong sense of self-knowledge with regards to their SLN label and spoke of direct, explicit supports, accommodations and therapies offered to them by supportive adults with specialized knowledge which contributed to positive behavioural, social-emotional, and learning adaptations based on their SLN label. We propose that several pedagogical changes need to be in place in order to help students build a stronger sense of self-knowledge. For example, specialized training in gifted education and inclusive teaching practices will help educators and parents understand the special learning needs of this population, which will aid in breaking down the myths surrounding the gifted label.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.951

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.064
GPT teacher head0.386
Teacher spread0.322 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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