Optimizing network infrastructure for streamlined information technology execution in a Federal Higher Education Institution
Bibliographic record
Abstract
This research aimed to analyze the structure of both formal and informal networks involved in Information Technology (IT) activities at the Federal University of Alagoas (UFAL). A survey was conducted to find the actors and assess the structural, relational, and centrality indicators within the formal and informal networks linked to IT activities at the university. The research employed document analysis, direct observation, and interviews with network members to set up the relationships among the actors in these networks and draw the main conclusions. The results revealed a significant disparity between the actors and indicators of the formal and informal networks. The informal network showed a larger number of actors and sectors involved, while the network density was low. This suggests the presence of untapped potential for exploring information, resources, and alternatives in the execution of IT activities. Additionally, it is recommended that future studies compare the networks and indicators based on their determinants to evaluate their impact on execution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".