Understanding the Homepreneurship Opportunities Afforded by Social Networking and Personal Fabrication Technologies
Bibliographic record
Abstract
The decreased cost and increased usability of personal fabrication technologies has enabled a new generation of crafters to integrate digital designs and computationally created artifacts into physically-based practices. With the simultaneous ubiquity of e-commerce and social networking channels, these technologies have enabled many crafters to transform their hobbies into home-based businesses. To understand the opportunities and challenges that fusing social networking platforms, personal fabrication equipment, and e-commerce have afforded these homepreneurs, an online survey and follow-up interviews were conducted with crafters who use hobbyist cutting plotters to personalize and sell goods online. The findings uncovered an emerging group of homepreneurs, i.e., mompreneurs, who use these technologies for supplemental income for their families and highlighted the emotional and opportunistic benefits that such personalized, at-home manufacturing affords. They also highlighted the workflows and lifestyle implications of using these technologies to run home-based businesses, the multi-faceted usage and dependence these homepreneurs have on online social platforms such as Facebook, the complex software toolchains that are used, and the commonplace practice of appropriating designs from others that occurs in this community.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".