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Enregistrement W136864177

The Draw a Scientist Test: A Different Population and a Somewhat Different Story.

2006· article· en· W136864177 sur OpenAlexaboutno aff
Mark D. Thomas, Tracy B. Henley, Catherine Snell

Notice bibliographique

RevueCollege student journal · 2006
Typearticle
Langueen
DomainePsychology
ThématiqueScience Education and Perceptions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychologyCONTESTTest (biology)PopulationSample (material)Developmental psychologyMathematics educationSocial psychologyDemographySociology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This study examined Draw-a-Scientist-Test (DAST) images solicited from 212 undergraduate students for presence of traditional gender stereotypes. Participants were 100 males and 112 females enrolled in psychology or computer science courses with a mean age of 21.02 years. A standard multiple regression generated a model that accounts for variability in sexes of drawings consistent with past findings. The focus of this research, however, was in comparing results of our sample (college students) with previous studies that have that have used DAST with much younger (e.g., elementary-aged) students. Results were strikingly similar, suggesting either that gender stereotypes are widely persistent even among college science majors, or that DAST may not be a particularly sensitive measure despite its wide use. ----- The a Scientist Test (DAST) was first utilized by Chambers (1983) to examine stereotypic views of scientists among school children. Chambers' initial study examined strength and presence of modern sanitized, and older mythic, stereotypic images of scientist in 4, 807 children's (ages 5 to 11 years) drawings that were collected from 1966 to 1977. Chambers assessed presence of lab coats, eyeglasses, facial hair, symbols of research, symbols of knowledge, technological aspects, and captions that were believed to represent a stereotypical view of scientists. Chambers reported that 49% of this sample consisted of girls, but participants produced only 28 drawings of female scientists (0.56%). All of these female scientists were drawn by female participants. Subsequently, Fort and Varney (1989) gathered 1, 654 drawings of scientists through a national contest for 2nd through 12th grade school students. Fort and Varney received 135 drawings of female scientists. They reported that, the 8% depicted by our respondents is close to reflecting reality, (p. 9) given their estimate that women then made up roughly 6% of scientific and engineering workforce at that time. A careful analysis of DAST images reported by Newton and Newton's (1992) survey of 1, 143 primary school children (ages 4-11) suggests children draw more females at a younger age, but by sixth school year, males were drawn by 83% of participants. Following up, Newton and Newton (1998) surveyed 1000 children from reception (the UK equivalent of preschool/kindergarten) to grade 6 and, ...concluded that there were few significant changes to primary pupils' conceptions of science and scientist... (p. 1148). Over time, number of female scientists drawn has slightly increased. This may be due to changes in social perceptions, or to refinements in measure. For example, Matthews (1994) had 132 children from years 7, 8, and 10 generate two different drawings, out of this total, 66% of images were male, while 34% were female. Likewise, Maoldomhnaigh and Mholain (1990) considered effects of test administration instructions, eventually changing DAST instructions to Draw a Man or Woman Scientist. Stated thusly, 367 children (299 females and 68 males) between ages of 11 and 16 years provided drawings. Although boys in this sample almost exclusively drew males, 49% of girls drew a female. Brosnan (1999) modified task by re-framing it as a, draw-a-computer-user-test. For Brosnan, whose sample consisted of 395 children ages 5 to 11 years, males performed similar to males who complete standard DAST. Interestingly, 70% of females drew a female computer user, while only 4% of males drew a female computer user. Variations of DAST have been utilized in U.S. and Canada (e.g., Parsons, 1997), Ireland (e.g., Maoldomhnaigh & Hunt, 1990), Finland (e.g., Raty, 1997), England (e.g., Brosnan, 1999), Korea (e.g., Song & Kim, 1999); and Taiwan (e.g., She, 1998) with similar results. A recurrent finding in DAST literature is that scientists are stereotyped as being male by girls and this may serve as a limiting factor in their self--efficacy toward becoming scientists. …

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)0,002
score de la tête « metaresearch » (Gemma)0,010
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,999
Score d'incertitude au seuil0,010

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,010
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,017
Tête enseignante GPT0,348
Écart entre enseignants0,331 · 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.

Devis d'étudeObservationnel
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

Citations43
Publié2006
Routes d'admission1
Résumé présentoui

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