Discovery of email communication networks from the Enron corpus with a genetic algorithm using social network analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the legal investigation of Enron Corporation, the U.S. Federal Regulatory Commission (FERC) made public a substantial data set of the company's internal corporate emails. This work presents a genetic algorithm (GA) approach to social network analysis (SNA) using the Enron corpus. Three SNA metrics, degree, density, and proximity prestige, were applied to the detection of networks with high email activity and presence of important actors with respect to email transactions. Quantitative analysis revealed that density and proximity prestige captured networks of high activity more so than degree. Subsequent qualitative analysis indicated that there were trade-offs in the selection of SNA metrics. Examination of the discovered social networks showed that density and proximity prestige isolated networks involving key actors to a greater extent than degree. In particular, density picked out interesting patterns in terms of email volume, while proximity prestige better isolated key actors at Enron. The roles of the particular actors picked out by the networks as reasons for their prominence are also discussed.
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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 | 0.001 |
| 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 it