Bibliographic record
Abstract
The usability of access control mechanisms in modern distributed systems has been widely criticized but little studied. In this paper, we carefully examine one such widely deployed access control mechanism, the one embedded in the WebDAV standard, from the point-of-view of an end-user trying to decide how to grant or deny access to some resource to a third party. This analysis points to problems with the conceptual usability of the system. Significant effort is required on the part of the user to determine how to implement the desired access rules; the user, however, has low interest and expertise in this task, given that such access management actions are almost always secondary to the collaborative task at hand. The analysis does however indicate a possible solution: to recast the access control puzzle as a decision support problem in which user intentions (i.e. the descriptions of desired system outputs) are interpreted by an access mediator that either automatically or semi-automatically decides how to achieve the designated goals and provides enough feedback to the user. We call such systems intentional access management (IAM) systems and describe them in both specific and general terms. To demonstrate the feasibility and usability of the proposed IAM models, we develop an intentional access management prototype for WebDAV. The results of a user study conducted on the system show its superior usability compared to traditional access management tools like the access control list editor.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".