Data Acquisition and Preparation for Social Network Analysis Based on Email: Lessons Learned
Bibliographic record
Abstract
Abstract : In sharing information to improve situational awareness, other government departments and remotely situated outposts may vary in their reporting of information. A social network analysis was initiated within the Department of National Defence to show where informal communication may be significant to information sharing. The study was undertaken circa Q32006 by the Experimentation Operational Research Team at the Canadian Forces Experimentation Centre for the Command and Sense Team. Analytical results are not available,as the undertaking was not completed. This report describes the lessons learned in planning the data collection and preparation for the social network analysis. The work was done under project Polar Guardian, the goal of which was to assess situational awareness in the arctic. The plan for the social network analysis included an initial email-based phase and a follow-up survey-based phase. This report focuses on the email phase; it is not a comparison of the two phases as separate approaches. Due to the short time frame for conducting the trial on the social network analysis approach, in-house methods for data acquisition and analysis were explored. The main challenges in this approach arise from generating the communications data from email tracking logs in isolation from other information gathering and information providing parts of a corporate computer network. Commercial tools were investigated, and warrant further examination. Their use requires a longer time frame for approval and installation on the Defence Wide Area Network. Of the commercial and home-grown approaches, the most time is likely needed for solutions involving access to the servers, and deployment of applications on them. Direct access to subject matter expertise in email administration is essential to arriving at a means for effective and timely data gathering and preparation.
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 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".