Positioning Human Microbiome DTC Tests On the Search for Health, Data and Alternatives Amid the Financialisation of Life
Bibliographic record
Abstract
Early during my fieldwork on the social life of the microbiome in Toronto, I was asked ‘Do you believe in microbiome testing?’ This question invited me to evaluate the science of the direct-to-consumer (DTC) test. In this Position Piece, I consider this question in a more expansive manner so as to position the test in its social and economic context. The distribution and public uptake of such a DTC test require scientific expertise but also marketing, capital investments, and clinical labour. This test requires consumers to do the work of stool collection and the reproductive labour of diet changes in their domestic spaces. I have learned that the microbiome is part of the quest for alternative ways of living and being healthy. Broadening the question to consider more than just the science expands the frame from one of scientific efficacy and individual consumption to one that considers the financialisation of health and the politics and environments of post-Pasteurian and post-industrial contexts.
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 | 0.031 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.088 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".