Theorizing the Costs of Self-Service Technologies and Co-Creation by Design
Bibliographic record
Abstract
In this commentary we explore how, in a market system that increasingly demands the participation of consumers as co-creators through self-service technologies, these technologies pose significant challenges to various consumers. We call this increase in demand the ‘everyday-ification’ of co-creation and consider its effect on consumers who are either unwilling or unable to co-create value. We look at how marketers are motivated to persistently replace human labor with technologies, not to primarily benefit consumers, but to discipline consumer labor and to maximize profits and shareholder value. Through this lens we examine five key issues with self-service technologies. First, we discuss how costs and benefits associated with self-service technologies are unequally allocated, before addressing how consumers’ choices are managed, consumers’ rising sense of powerlessness and increased vulnerabilities, consumers’ service failure responsibilization, and the cybernetic bureaucracy of life through self-service 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.038 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| 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".