Lack of Congruence between Analyses and Conclusions Limits Usefulness of Study of Socio-cultural Influences on Student Choice of LIS
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.254 | 0.538 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.027 | 0.018 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".