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How can we better identify the hidden intellectually-creative abilities of the gifted?

2008· article· en· W1564998999 sur OpenAlexaff
Larisa V. Shavinina

Notice bibliographique

RevuePsychology science · 2008
Typearticle
Langueen
DomainePsychology
ThématiqueCreativity in Education and Neuroscience
Établissements canadiensUniversité du Québec en Outaouais
Organismes subventionnairesnon disponible
Mots-clésPsychologyContext (archaeology)CognitionMetacognitionIntelligence quotientCognitive psychologyPsychological testingEducational psychologyTest (biology)Cognitive developmentDevelopmental psychology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Abstract This article proposes a new approach to the psychological assessment of potential intellectuallycreative abilities of the gifted based on the new cognitive-developmental theory of giftedness developed by the author. The major limitations of conventional intelligence tests are shortly analyzed. The nine methodological and procedural principles, which constitute this approach, are presented along with the examples of new intelligence tests. The principles state that new intelligence tests should first of all examine the psychological mental context generated by gifted individuals themselves. These tests should have an open character, evaluate the basis of giftedness (not its numerous traits or manifestations), and allow both retrospective and prospective assessment. New tests should not evaluate psychological functions/processes (e.g., attention or memory) and mental speed, and they should not be very long or time-consuming. Cognitive styles, metacognitive and extracognitive abilities should also be assessed. Child's sensitive periods - which form the developmental foundation of giftedness - should be examined as well. Key words: Cognitive-developmental theory of giftedness, psychological mental context, retrospective and prospective assessment, methodological and procedural principles, assessment of intellectual abilities. Introduction Linda Silverman (2008) convincingly demonstrated that intelligence tests were, are, and will be the major instrument used to assess an individual's intellectual abilities and thus will remain the main tool to identify the gifted. Intelligence tests have been one of psychology's important technological innovations since the last century. Although modern information technology leads to the emergence of new technological innovations in psychology - for example, technologies related to cyberpsychology (Shavinina, 1998, 2000a, 2000b) - intelligence tests continue to be its most traditional and widespread technology. The problem with intelligence testing is that it is not developing very fast (Daniel, 1997; Flanagan & Alfonso, 1995; Esters, Ittenbach, & Han, 1997; Shobris, 1996; Sternberg & Kaufman, 1997; Sternberg, Wagner, Williams, & Horvath, 1995). The reasons for this have been thoroughly identified in the literature (Sternberg & Kaufman, 1997). The lack of satisfactory theories of human intelligence and intellectual giftedness, upon which any development of new assessment methods is based, is also one of the reasons. To understand the nature of human intelligence and intellectual giftedness means to understand what intelligence tests should measure, and how, as well as how to better identify the hidden intellectually-creative abilities of the gifted. Intelligence testing may advance by being strongly influenced by current scientific data in general and by recent research findings from the psychology of high abilities in particular. This article presents one such attempt. It should be emphasized that this is the attempt to move the field of giftedness forward in the direction of the comprehensive assessment of high abilities, and specifically the measurement of potential gifts and talents of everyone. It is a disturbing reality that we do not have reliable and exact assessment methods, that would allow us to identify (not lose!) the hidden abilities of children and adolescents. Many examples demonstrate that giftedness of many geniuses and other highly accomplished individuals were overlooked in their early years. Albert Einstein is probably the best known of them (Shavinina, 2008a). This is an alarming thought that even today, more than a hundred years after Einstein's childhood, many individual gifts and talents are going to be lost because of a lack of appropriate assessment. If one thinks for a while about the impact of the gifted on society in general (Shavinina, 2008c) and their unique innovative abilities in particular (Shavinina, 2008b), then it is clear that the assessment of high ability is an extremely important scientific topic and the task of developing comprehensive and ideal identification methods is a great job for giftedness researchers of the future. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,022
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,031

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,022
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0050,001
Études des sciences et des technologies0,0010,004
Communication savante0,0040,008
Science ouverte0,0010,003
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,072
Tête enseignante GPT0,381
Écart entre enseignants0,309 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2008
Routes d'admission1
Résumé présentoui

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