Consumer Choicemaking and Choicelessness in Hyperdigital Marketspaces
Bibliographic record
Abstract
Technologies, especially Internet-based digital ones, are reshaping choice processes – actual considerations and actions, as well as perceptions of these – in massive, often fundamental, ways. In this paper, our goal is to explore choice processes in general, and especially choice processes in hyperdigital marketspaces (i.e., with massively, pervasively interconnected things) with examples drawn from U.S. macro consumption contexts. We start with a short review of discourses on choice and choicelessness and then shift to the emerging era of technology-shaped choice processes that are especially observable in contemporary hyperdigital marketspaces. For the increasingly large swaths of market segments that consume, indeed live, digitally, we find deft symbolic sublimations and inversions happening, wherein manipulation is perceived as autonomy enhancing.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".