Values as Hypotheses: Design, Inquiry, and the Service of Values
Bibliographic record
Abstract
Values: A Problem of Practice The question of the relationship of design and values has sparked much scholarship during the past 30 years.These investigations have led to the growing consensus that design is not a neutral activity; rather, it is value-laden: design is laden with, or bears, values.Despite substantial agreement that design is value-laden, significant variation arises in understanding how and why design bears values.1 Some scholars argue that artifacts act to determine what is possible and impossible in human engagements with the world -that is, products bear consequences that affect what we value in human life and living.2Others note that products, broadly conceived, bear the conscious and unconscious intentions, values, and politics of the individuals and corporations that designed them.3Some scholars propose that designed products bear the preferences and values of those who use them,4 while others view values as ideals, and design bears the burden of approximating an ideal.5Others speak of products as embodying values, as valuebearing material expression.6Others emphasize the capacity of designers and publics to give voice to values, to contest and argue for what should be valued; here, values are born and borne in argument.7None of these positions offers a definitive, settled, or uncontested account of the relation of design and values.This scholarship, however, has led to calls for practitioners to explicitly address values in their everyday design practice.Values-oriented practitioners not only are faced with a variety of theoretical understandings; they also regularly encounter the empirical fact that a given value (e.g., autonomy) can be both valuable and not valuable in its participation in design products and practices.Batya Friedman provides a useful example that illustrates this problem.She describes a situation in which a new computer workstation, designed to support speech input and multimedia, includes a built-in, always-on microphone.When a user of this workstation wishes to have a conversation that is not recorded, she must go through multiple steps to turn off the microphone -a cumbersome solution.Out of this case, Friedman explores the concept of autonomy, she asks:
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 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 | 0.134 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.155 |
| Scholarly communication | 0.024 | 0.043 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".