Problem of Ethics in Librarianship and Approach in Turkey
Bibliographic record
Abstract
Ethical standards in the field of librarianship were accepted for the first time in 1938 by the American Library Association (ALA) in the USA. During the follow- ing years, even though some work has been carried out in this field in countries such as Canada, Japan and England, the subject was beginning to be considered as a major problem of international level only during the 1990s. In the 1990s many library associations identified and implemented their ethical standards. Ethical approaches in the field of librarianship in Turkey began appearing on the professional agenda during the second half of the 1990s and the "Professional Code of Ethics" was adopted by the Turkish Librarians' Association (TKD). It is observed that those ethical principles determined by the TKD are not fully reflected in the implementation process. Despite the fact that 11 years have elapsed since the determination of ethical codes, these principles have never been used by informationprofessionals because the subject of "ethics" is still not perceived as a fundamental professional issue. The research will deal with the definition, the scope, the limits and the professional reflections of librarianship ethics; its implementation dimension; and historical developments on the international level and in Turkey together with their outcomes. The study will also emphasize the concepts of "ethics" and "professional ethics" to assure correct comprehension and interpretation of "librarianship ethics".
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".