Overcoming the digital tsunami in e-discovery: is visual analysis the answer?
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
New technologies are generating potentially discoverable evidence in electronic form in ever increasing volumes. As a result, traditional techniques of document search and retrieval in pursuit of electronic discovery in litigation are becoming less viable. One potential new technological solution to the e-discovery search and retrieval challenge is Visual Analysis (VA). VA is a technology that combines the computational power of the computer with graphical representations of large datasets to enable interactive analytic capabilities. This article provides an overview of VA technology and how it is being applied in the analysis of e-mail and other electronic documents in the field of e-discovery, as well as discussing several challenges and limitations of the technology. The article concludes that VA has the potential to overcome some of the limitations of current search and retrieval techniques, but that addressing the digital tsunami is more likely to be achieved by using VA in combination with other search and retrieval technologies in the context of creating an effective data governance program.
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.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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