Ethical consumption? There's an app for that. Digital technologies and everyday consumption practices
Bibliographic record
Abstract
Ethical consumption mobile phone apps are increasingly popular. These apps allow consumers to scan the barcodes of products they are considering purchasing and determine whether or not they align with their ethics. App technologies are often applauded for their potential to provide consumers with targeted, crowd‐sourced information about products while shopping and to foster more political, and less individualistic, consumption practices by connecting users to one another and to campaigns. There is a growing field of scholarship conceptually examining the role of information and digital technologies in ethical consumption. However, there is little empirical research on how consumers engage with ethical consumption apps in everyday ways. Drawing on an in‐depth study with 21 participants, this paper explores how app use mediates people's experiences of ethical consumption. We contend that the app design structures and limits how individuals engage in ethically motivated consumption and influences their conceptualizations of ethical consumption as a political practice. We conclude by illustrating that critically examining what it means to be “ethical” in a digital world is a crucial area of research for geographers, particularly as these ethics play out through the everyday use of mobile technologies.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".