Refrigerator Wisdom: Social Rules and Rights as a Conceptual Framework for Digital Ethics
Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.Is it possible that a kitchen appliance can teach lessons about data responsibility? When faced with challenges that come with new innovations, domestic material objects can provide useful conceptual frameworks of understanding, if we can recognize them. One approach is to apply the concept of refrigerator wisdom, defined as tacit knowledge of boundaries, rights, accessibility, fairness, reciprocity, and resilience, gained through our daily interactions with the now-ubiquitous, century-old technology of the ordinary household refrigerator. This paper positions the refrigerator as a metaphorical model by identifying the well-established and commonplace rules governing its use into a theoretical framework for ethical and responsible data engagement. This model provides the basis for ten principles - what I have called the refrigerator manifesto - that demonstrate how the socially codified rules of one technology can be applied to digital projects and the ethical use of data. These principles demonstrate the utility of identifying how ethical rules and social boundaries overlap across technologies, and explore themes of permission, labelling, reciprocity, accessibility, storage, waste, and dependency.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.085 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| 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".