Lack of Congruence between Analyses and Conclusions Limits Usefulness of Study of Socio-cultural Influences on Student Choice of LIS
Notice bibliographique
Résumé
A Review of: Moniarou-Papaconstantinou, V., Tsatsaroni, A., Katsis, A., & Koulaidis, V. (2010). LIS as a field of study: Socio-cultural influences on students’ decision making. Aslib Proceedings: New Information Perspectives, 62(3), 321-344. Objective — To determine how social and cultural factors influence students’ decision to study library and information science (LIS) as undergraduates. Design — Semi-structured interviews and quantitative analysis of questionnaire data. Setting — Three schools in Greece with LIS programs at the undergraduate level. Subjects — One hundred eighty-seven first-year students enrolled in Greece’s LIS schools’ undergraduate programs in the autumn semester of the 2005-2006 academic year. Methods — The authors piloted the questionnaire with 52 students at the LIS school in Athens and had three faculty members review the questionnaire. After modification, the two-part questionnaire was administered during the first week of classes to all first-year undergraduate students enrolled in Greece’s three LIS schools. The first section of the questionnaire collected data on student gender, age, area of residence, school from which they graduated, and parental occupation and level of education. The second part of the questionnaire covered students’ reasons for choosing LIS as a field of study, the degree to which students agreed with dominant public views (i.e., stereotypes) of librarianship, and practical issues that influenced students’ decision-making processes. The authors conducted two rounds of semi-structured interviews with students from the same 2005-2006 cohort. They interviewed 41 self-selected students and then interviewed a purposive sample of 15 students from the same cohort in the fifth semester of the students’ studies. Main Results — The questionnaire was completed by 187 LIS students, with 177 responses considered relevant and used in the analyses. Demographic information showed that 78% of the respondents were female, 85.8% were from urban areas, and 98.9% graduated from public schools. The authors constructed two indices to assist with further analyses: the Educational Career Index, which quantified students’ educational experience prior to study at the university, and the Divergence Index, which was created by comparing students’ university entrance exam scores and students’ ranking of LIS as a preferred field of study. The authors determined that 65% of the variance in the data was explained by two factors: students’ responses to library stereotypes and students’ self-reported reasons for choosing to study LIS. The self-reported reasons for studying LIS were combined into four variables (extrinsic reasons, intrinsic professional reasons, intrinsic academic reasons, and intrinsic social reasons) to be used in the multivariate analysis of variance tests (MANOVAs). Three distinct clusters of students were found using the indices and parental education level in cluster analysis: Cluster 1 (low parental education, low Educational Career, and low Divergence indices scores), Cluster 2 (intermediate parental education, high Educational Career, and low Divergence scores), and Cluster 3 (high parental education, high Educational Career, and low Divergence scores). For three of the factors for choosing the LIS field (intrinsic professional reasons, intrinsic academic reasons, and intrinsic social reasons), Cluster 1 showed statistically significant differences (p
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,254 | 0,538 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,027 | 0,018 |
| Études des sciences et des technologies | 0,005 | 0,009 |
| Communication savante | 0,012 | 0,013 |
| Science ouverte | 0,006 | 0,010 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,003 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».