Bibliographic record
Abstract
School librarians have always strived to teach information ethics to students. The importance of conveying the concept of information ethics to students has become critical within the overwhelming effect of data and information flow. In an age of massive information flow we need to address the issues related to that subject: ethics vs. wrongdoing, propoganda vs. accuracy, bias vs. neutrality, prejudice vs. objectivity, self opinionatedness vs. flexibility, fiction vs. documentary, fact vs. opinion. Turkish communities largely depend on oral culture. The codes of a written culture maybe unknown to some part of the population. In Turkey generally school librarians are not expected to teach. Most schools may not even have a certified librarian. Those who happen to work at schools are not expected to have a teaching certificate. Teachers at schools who speciliaze on subjects may teach about ethics, in case the issue is in question. Primary years programme has scheduled the subject Thinking in education which involves a chapter on citizenship and democracy education; the subject is offered as a elective. High school grades 10 and 11 will study philosphy but Being, Ethics, Art and Religion topics are undervalued. We as school librarians can fill in the gaps. We need to be role models in teaching media literacy, academic honesty and integrity, and above all the culture of information. Collaboration with teachers is a necessity, teaching information ethics related to the student lives, with direct examples drawn from their life- experiences are very important. If we teach the subject in a dull way, with irrelevant examples it makes no sense to them and they can easily disconnect. We need to adddress the issue of using text online, voice call, images and videos, the differences and similarities. We need to be information curators for our communities.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".