The importance of context in the automatic classification of email as records of business value: A pilot study
Bibliographic record
Abstract
Abstract Despite the growth of alternative means of electronic messaging, email continues to be a crucial business communication tool. In many organizations, it is common to find a large proportion of business‐critical data in email form. However, as the volume of business‐critical email continues to grow, conventional approaches for managing paper‐based records of business value are increasingly unsuited to the digital realities. Yet effective approaches to automate the management of email are essential to ensure organizational efficiency, accountability and regulatory compliance. Our research aims to develop a model that characterizes information managers' context‐specific email triage strategies for identifying emails of business value so that it can be applied to automatically classify email records. We conducted a pilot study of two information managers' email triage practices in two different contexts and develop machine‐learning models of lexical and non‐lexical features that are involved in the appraisal of business value. An experiment with a machine learning algorithm trained on about two hundred emails in two different business contexts indicates that the automation of email triage for identifying records of business value is highly context dependent and that automated classifiers must be trained to recognize “business value” specifically for that context.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 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 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".