Bibliographic record
Abstract
<div> Do-It-Yourself (DIY) biology, also known as biohacking or community biology, is a grassroots movement where people conduct biological experiments outside formal institutions. DIY biologists set up their laboratories in garages and community spaces and often acquire their equipment and materials from online marketplaces. While their needs for material resources are comparable to those of academic and industrial laboratories, DIY biologists face greater challenges in acquiring such resources, reflecting the structural disparities between institutional and extra-institutional science. This paper examines how DIY biology spaces are materialized and explores the challenges encountered during this process. Materializing refers to the efforts of DIY biologists to transform their visions of grassroots science into material and immaterial results. Drawing on semi-structured interviews with 23 DIY biologists across Great Britain, Germany, and Canada, alongside observations from online and in-person events, this study highlights how these practitioners position themselves within their respective countries’ life science landscapes. The findings indicate that DIY biologists actively negotiate boundaries between institutional and extra-institutional science, engaging in debates over funding sources, including partnerships with industry, and seeking alternative models of community sustainability. Country-specific innovation and economic ecosystems shape these negotiations. For instance, Canadian DIY biologists embrace entrepreneurial narratives akin to ‘garage start-ups,’ aligning with a neoliberal ethos of innovation. In contrast, their German counterparts gravitate toward the principles of <i><i>Mittelstand</i></i> entrepreneurship emphasizing stability, regional embeddedness, and responsibility. Despite their diverse approaches, DIY biologists in all three contexts face systemic challenges tied to neoliberal funding structures, particularly a scarcity of financial resources. These insights contribute to understanding how grassroots science adapts to, and resists, broader socio-economic forces, illuminating the dynamics through which science is materialized outside traditional institutions. </div>
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.772 | 0.010 |
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; both teacher heads agree on what is shown here.
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".