Bibliographic record
Abstract
A Review of: Pinto, M., Sales, D., & Fernández-Pascual, R. (2019). Gender perspective on information literacy: An interdisciplinary and multidimensional analysis within higher education settings. Library & Information Science Research, 41(4), 100979. https://doi.org/10.1016/j.lisr.2019.100979 Abstract Objective – To identify gender differences that present in 26 information literacy (IL) learning competencies using a multidimensional subjective–objective approach. Design – Two quantitative survey questionnaires, administered online. Setting – Five Spanish public universities in 2014. Subjects – Third- and fourth-year students in eight social science degree programs including information science, audiovisual communication, journalism, psychology, primary education, pedagogy, social work, and tourism. Methods – Subjects were recruited using a stratified sampling approach. Two survey instruments were distributed online. The IL-HUMASS instrument uses Likert scales to measure students’ “belief-in-importance” (BI) of various IL competencies relating to searching, evaluation, processing, and communication–dissemination, as well as their self-efficacy (SE) regarding these competencies. The EVALCI-KN instrument measures students’ actual knowledge (KN) of the same IL competencies using closed answer options. The data were analyzed using descriptive and bivariate statistics and confirmatory factor analyses. Main Results – The total number of valid surveys collected was 1,575 (sampling ratio of 10.39% of eligible students). No significant differences were found between female and male students’ BI, SE, or KN in the categories of searching and evaluation. Statistically significant differences between genders were found relating to SE and knowledge of information processing (with men having higher scores), and to knowledge of communication–dissemination (with women having a higher score). Overall, students’ KN scores were higher than their SE scores. Statistically significant differences were found among male students in all categories and dimensions except in SE of evaluation and BI of communication–dissemination and among female students except in BI of processing. Information science and pedagogy were the highest scoring degree programs in different dimensions and categories; tourism and social work were the lowest. Male students’ awareness of the importance of using print sources and assessing the quality of information could be improved; female students’ awareness of the importance of knowing information source typologies, academic codes of ethics, and intellectual property laws could be improved. The authors also state that male students’ KN should be increased in the areas of schematizing and abstracting information, handling statistical programs, and knowing the laws on information use and intellectual property, and they point to the need for instructional support to improve all students’ SE across all IL categories. Conclusion – Gender differences were found in various IL competencies as measured by the three scales (BI, SE, KN). Male students were found to believe assessment skills to be most important and to believe themselves more prepared in search skills; however, their actual knowledge was highest in the communication category. In comparison, female students prioritized communication skills and believed themselves more prepared in search skills, with their actual knowledge highest in the search and communication categories. Among both genders, weaknesses were found relating to BI in four competencies (use informal electronic sources, know information search strategies, schematize–abstract information, recognize text structure), to SE in six competencies (use printed sources, know information search strategies, assess quality of information, schematize–abstract information, recognize text structure, write a document), and to KN in five competencies (use printed sources, use electronic sources, use informal electronic sources, determine whether information is updated, and know the code of ethics in the academic field). The students’ mean score was higher for KN than for SE in searching, evaluation, and communication–dissemination. The authors recommend instruction or awareness-raising sessions to help students acquire IL competencies as well as to improve their self-esteem in these areas, with the design of these sessions incorporating the findings on gender differences. They also recommend a review of existing syllabi to help “incorporate the gender perspective into the classroom” (p. 8).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.218 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; a candidate call from one teacher head, not a consensus.
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".