Bibliographic record
Abstract
Tools for computer supported collaborative work offer many advantages for their users. Some developments in this field have included agent-based approaches for CSCW. Many challenges face the development and application of distributed, agent-based solutions for CSCW. A concern often overlooked is the acceptability and social impact of these technologies. In particular, the management of the privacy of collaborators is a paramount issue that must be accommodated. Included and related to the privacy of user information and interactions are assurances of integrity, certification, validation, ownership, non-repudiation, and authentication of individuals and software systems operating on disparate computers and operating systems, communicating over heterogeneous networks. We outline our early work on an approach for accountable privacy that may be applied to distributed computer supported cooperative work (CSCW) environments. Particular challenges for the issue of privacy associated with the CSCW application scenario are discussed.
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 | 0.052 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.018 | 0.031 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".