Supporting Organizations in Improving Employee Bulk E-mail --- A Tool Design and Evaluation Study
Bibliographic record
Abstract
Organizations often send bulk emails to employees to make them aware of policy changes, organization plans, and events. Many of these emails, however, are long digests with many separate messages that waste employees' time and reduce their awareness. This study introduces CommTool--a prototype tool to help organizational communicators better understand their emails' performance and cost. We first interviewed 5 communicators and identified the need to measure the performance of each message within bulk email. Then we iteratively designed and deployed an organizational bulk email evaluation platform (CommTool), which enables communicators to get diverse message-level metrics such as reading time, relevance rate, comments, etc. We evaluated these designs through a 2-month field deployment with 5 communicators and 149 organization employees. We found that 1) the message-level metrics, such as reading time and relevance rate, helped communicators understand their audience and design bulk emails; 2) the cost and reputation metrics did not influence the organization leaders' decisions. We summarize with suggestions on designing organizational bulk email evaluation platforms that provide message-level performance and cost information.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".