E-litter: Nudging Email Usage Behavior One Byte at a Time MHCI007
Bibliographic record
Abstract
The ubiquity of technology and the “zero-cost” nature of cloud services result in users overlooking the environmental impact of their online usage. While some cloud services (e.g., LLM inference) have significantly higher environmental impact, this work focuses on emails, a service widely used yet representing one of the smallest usages of cloud resources. Just as recycling a piece of paper may not have a great impact but can contribute to environmental awareness, deleting emails symbolizes a small yet meaningful behavior change - a ’gesture’ toward environmental responsibility. In this work, we propose E-litter, a mobile system combining a bulk-delete UI with eco-feedback to encourage email deletion. By representing email storage in tangible terms (e.g., sheets of paper), E-litter nudges users to become more aware of cloud usage. A user study with Gmail users shows that E-litter significantly increases email deletion, highlighting the potential of UI and eco-feedback in impacting cloud usage behavior.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".