Self-quantification and the datapreneurial consumer identity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study delivers a clearer understanding of the constitution of the datapreneurial consumer, the role of the market in that construction, and the implications for consumer identity projects in the age of Big Data and an increasingly data- and surveillance-driven society. The study uses a theoretical framework of the “quantified self” (QS) to examine consumers (re)building creditworthiness. In the context of a major online credit-user forum, it employs creative-nonfiction methodology to protect forum-member privacy. To the literature on creditworthiness, the study contributes a process model of the construction of the datapreneurial credit consumer identity. To the QS literature, it offers insight into how consumers may embrace quantification and self-tracking, even in areas where they are nudged or pushed into it. To the sociology of quantification literature, it adds empirics to explain how consumers may embrace market-provided self-quantification resources in attempts to liberate themselves from the structural control of that very quantification.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it