The unexpected reason firms should institute policies to remove email signatures: Quantifying human mortality costs of email signature-based reputation signaling
Bibliographic record
Abstract
It has become fashionable in some corporate and academic circles to reputation signal by amending pronouns and/or land acknowledgements to email signatures. Extra information exchange, however, has environmental and social impacts including human mortality from climate destabilization. To illustrate the human mortality cost of carbon-emitting information technology the 1000-ton rule can be used to quantify the cost in human lives. In this study the two types of additional information used in reputation signaling for i) pronouns and ii) land acknowledgments are analyzed by the 1000-ton rule for a case study of Canada. The results of the carbon emission induced human mortality from adding only 3 words in emails to identify gender in a relatively small nations like Canada (∼40 million people) with only a small fraction adding pronouns (∼15 %) are still responsible for prematurely killing a person per year. Likewise, if Canadians all used land acknowledgements in their emails roughly 30 people would be sacrificed annually to reputation signaling. Based on the results of this study the environmental harm and human mortality caused by current information technology infrastructure is such that adding even a few words to an email signature represents an ethically and morally unacceptable human sacrifice. As most of the content of signatures is redundant (far more so than reputation signaling), polices are recommended that signatures are replaced with a hyperlinked name to vital information. To increase efficiency of digital information transfer further policies could eliminate most signatures entirely as emails already identify senders in the header.
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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 | 0.008 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".