Mobile communication and ethics: implications of everyday actions on social order
Bibliographic record
Abstract
Of the many opportunities and affordances that mobile technologies bring to our day-to-day lives, the ability to cheat physical separation and remain accessible to each other—in an instant—also brings pressure to bear on well-established social conventions as to how we should act when we are engaged with others in shared spaces. In this paper we explore some ethical dimensions of mobile communication by considering the manner in which individuals in everyday contexts balance interpretations of emergent social conventions with personal desires to connect in the moment. As we later discuss, the decisions made in response to a ringing mobile phone or flashing text message emerge from consequential versus deontological ethical frames used to determine what to do versus what we ought to do. This is particularly true in western and North American cultural contexts from which our data are collected. Using Goffman's dramaturgy, we suggest that these conflicts occurring on an individual level provide evidence of social structure, and are simultaneously entwined with our less obvious ruminations on the maintenance of social order.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.072 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".