Attitudes of UK Librarians and Librarianship Students to Ethical Issues
Bibliographic record
Abstract
There have been a number of studies examining the attitudes of librarians to ethical dilemmas, but few examining them in comparison with Library and Information Science students as we did in our study. According to that UK librarians and students in general hold surprisingly similar ethical attitudes. We expected the students to be more liberal, more willing to uphold idealistic principles, and given their student status, with attitudes balanced in favour of other students' and patrons' rights in terms of fees, and accessibility, and copyright law. On the contrary, in many areas such as Internet filtering, looking at online erotic images, and removing books at the request of patrons, we found practitioners more liberal than the students. A reason for that might be that the students are keen to emulate what they perceive to be a conservative and mature outlook, i.e., a stance of responsibility, as a pressing concern for ILS students is likely to be the establishment of a career. Though there is a fair level of teaching ethical issues it seems to lead into a mediocre level of student awareness of basic issues or of the CILIP Code which is meant as a 'framework' to help information professionals 'manage the responsibilities and sensitivities which figure prominently in their work' (CILIP 2003).
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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; 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".