The Impact of Social Responsibility and Organizational Accountability on the Performance of Public Librarians in Iran
Bibliographic record
Abstract
A Review of: Soltani-Nejad, N., Jahanshahi, M., Karim Saberi, M., Ansari, N., & Zarei-Maram, N. (2022). The relationship between social responsibility and public libraries accountability: The mediating role of professional ethics and conscientiousness. Journal of Librarianship and Information Science, 54(2), 306–324. https://doi.org/10.1177/09610006211014260 Objective – To determine how librarians' perceptions of public libraries' social responsibility and accountability within society affect their professional ethics and conscientiousness. Design – Quantitative, survey questionnaire. Setting – Public libraries in Iran. Subjects – Survey respondents (N=362) were public librarians* (see note below). Methods – The authors created a theoretical model based on six separate hypotheses, describing the relationship between the following variables: corporate social responsibility, organizational accountability, professional ethics, and conscientiousness. A questionnaire was distributed to the participants. SPSS 21.0 was used for the analysis of demographic data and SMART PLS 3.0 was used to assess the theoretical model. Main Results – The results show a significant, positive, and direct relationship between the variables being studied (corporate social responsibility, organizational accountability, professional ethics, and conscientiousness), therefore confirming the relevance of the authors’ theoretical model. Conclusion – The results of this study demonstrate the importance of promoting the social responsibility and organizational accountability efforts of public libraries. The data suggest that doing so will strengthen the positive perception of the library amongst employees, which will in turn have a positive effect on their professional ethics and conscientiousness. The authors suggest that library managers need to create a culture of accountability and ethics within libraries. They can do so by incorporating ethics and social responsibility in decision-making and policies. Additionally, the authors propose that professional ethics training in library curricula and continuing education would provide librarians with the knowledge necessary when encountering ethical dilemmas on the job.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".