Bibliographic record
Abstract
Do we need global ethics for the net? Is it even possible to put these into the form of a universal agreement, embodying the necessary rules and principles in an all-encompassing code of conduct? Or will any such endeavors simply shatter on the differences of cultures? Ought they be labeled as sort of attempted imperialism, more subtle perhaps in comparison with other forms of cultural imperialism—but nevertheless an attempt of such? If so, then ethical concepts need to be restricted to territorially or ethnically specific realms. In that case, the quest for Net-Ethics could perhaps be substituted by pragmatic actions: instruments of control that are simply technical and formal, devoid of moral input and moral convictions. Such a viewpoint has been offered lately in the form of a concept by the Bertelsmann Foundation, which combines rating and filtering instruments with a social lattice-work of net-supervision and transnational combinations of institutions of control. That concept indeed has its charm. It has its problems too; problems that can be made obvious from an ethical viewpoint and which counter the notion, that it is possible to supplant moral instances with pragmatic action. The text therefore reconstructs the concept in question, criticizes it and attempts to sketch an ethical approach to the problem that respects diversity and plurality.
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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.059 |
| Scholarly communication | 0.014 | 0.036 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 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".