UNDERSTANDING SYSTEM ADMINISTRATORSâ WORK PRACTICES AND THE ROLE FOR ENHANCED VISUALIZATIONS IN THEIR TOOLS
Bibliographic record
Abstract
Visualization can be an effective way to explore and understand abstract data. Due to the rapidly changing technological environment of sys admin work and the scale of data involved, enhanced visualizations might provide benefit in this domain; however, despite research efforts, to-date the tools for system administrators (sys admins) minimally employ the use of interactivity in models and provide limited visualizations in tools. This may be because sys admins have a culture of command-line interface (CLI) use that is at odds to the graphical user interface (GUI) that accompanies most tools that incorporate interactive visualizations. We designed a two phase study to gain a better understanding about the work of sys admins, their current tool environment, their preferences for CLI and GUI based tools, and their perspective about how the inclusion of interactive visualizations in tools and system models might enhance their routines. The first phase of contextual inquiries and semi-structured interviews with 37 participants gave us a rich understanding of system admin work practices and their desired functionality for future tools. In the second phase, an on-line survey with 331 sys admins allowed us to generalize our findings. Based on our research, we generated recommendations for desired tool features in each of the sub-domains of sys admin work (i.e., network, virtualization etc.,). We also conducted an analysis of the type of visualizations that could be implemented in future tools to support the challenging nature of sys admin work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".