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 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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".