The Quest for a Clean Slate Building and Protecting Reputation in the Cyberworld
Bibliographic record
Abstract
ICT technology has multiplied the possibilities for presenting who one is in the Cyberworld. The means for creating, maintaining but also of losing a good reputation have increased exponentially with an international audience now just a click away. However, these means can also be employed for abusive or, at least, purposes for which they were not intended, with undesired revelations, cyber-bullying and the creation of fake identities potentially ending in cyber-homicide. The Quest for a Clean Slate thus comprises multiple obstacles at various levels much like an adventure video game; no sooner are the obstacles, opponents and traps defeated or overcome and the level accomplished, than the next level begins presenting a whole host of new challenges and threats. The reputation warrior, equipped with a sword entitled "freedom to self-determination" and a humble shield entitled "legal redress", is thus thrown into the ever expanding and changing landscape of swamps and wilderness that is the Cyberworld. This paper attempts to present a sneak preview into the various levels of the Quest for a Clean Slate, the online reputation game, depicting its challenges, pitfalls and the possible means for overcoming these latter.
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| 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".