Bibliographic record
Abstract
How do people make imperfect but inescapable data-driven technologies work? Beyond tinkering and gaming, I contribute the concept of ‘data auxiliaries’ to elucidate an emerging workaround strategy individuals deploy. More specifically, data auxiliaries can be conceived as personalized, vernacular systems devised to optimize user objectives and ameliorate the effects of data blind spots when interfacing with data-driven technologies. Here, I use the rise of dating spreadsheets as a case study to show how data auxiliaries make online dating ‘work.’ Drawing on interviews with 42 ‘love hackers’ and an analysis of 24 dating spreadsheets, I demonstrate through three processes – problematizing, tailoring, and sense-making – how spreadsheets allow individuals to track and use important experiential and relational data points that dating platforms do not take into account. Beyond bridging an intimate connection between personal experience and data, dating spreadsheets help love hackers regain a sense of control by providing structure to love’s numbers game. However, translating analytical insights from data auxiliaries to behavioral shifts proved difficult due to interpretative challenges. By demonstrating the emotional benefits and practical limits of data auxiliaries, this article concludes with implications for understanding the role of self-datafication in an era of big data supremacy.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".