Information Ethics in a Different Voice, Or: Back to the Drawing Board of Intercultural Information Ethics.
Bibliographic record
Abstract
Within the information ethics community one can observe a mainstream discussion including some fundamental presuppositions which appear to be something like dogmas. The most important of these dogmas seems to be that we must create a new kind of intercultural information ethics. It is often argued that (comparative) studies have shown that different cultures, according to culturally determined norms and values, react in different ways to the impacts of ICT; it is stressed that an intercultural information ethics must take these cultural particularities into account. But in the paper at hand it shall be argued that taking cultural differences into consideration does not create a necessity to invent a new intercultural information ethics. On the contrary it shall be claimed that we already know several intercultural ethics which only have to be applied to ICT and its impact to societies.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 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".