Bibliographic record
Abstract
Without knowing the rules of the game in a specific area qualified ethical decisions within are simply not possible. Therefore, a fundamental understanding of the phenomenon 'media' is a prerequisite for the ‘usablity’ of any media ethics. This understanding of the very basis of media is introduced by the notion of space: media is the space where the presentation of something is possible – formally that space fulfils the criteria of a Hilbert space; more common is this concept in the notion of cyberspace e.g.. As presentations (in a real as well as in a Hilbert space) do not exist separated from each other but are (more or less) connected, are linked, thus acting in the media can be understood as moving in the space of communication, following the links. This understanding leads to fundamentally new ethical categories: media ethic is the challenge of ethically designing the communicative space. The appropriate ethical approach for this task can be found in Wittgenstein's notion of 'use'. That overcomes the 'blind' application of ethical norms to categorical distinctive descriptions, which is very common through the term 'applied ethics' though it can be easily proved as aporetic. The investigation in what the ethical 'ought' basically could mean shows that there is no normative meaning without a descriptive context: 'to ought' always means 'to ought' in a certain situation, be it more common or more specific. Ethically right then means: one can decide on the basis of good reasons to do the right in this or that situation. And ethics finally is the quest to categorize, structure and systemize these right decisions by the means of creating a comprehensive theory. These are the limits and the dignity of ethics as a scientific scholarship and media ethics in particular.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.062 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".