"Who owns your poop?": insights regarding the intersection of human microbiome research and the ELSI aspects of biobanking and related studies
Bibliographic record
Abstract
BACKGROUND: While the social, ethical, and legal implications of biobanking and large scale data sharing are already complicated enough, they may be further compounded by research on the human microbiome. DISCUSSION: The human microbiome is the entire complement of microorganisms that exists in and on every human body. Currently most biobanks focus primarily on human tissues and/or associated data (e.g. health records). Accordingly, most discussions in the social sciences and humanities on these issues are focused (appropriately so) on the implications of biobanks and sharing data derived from human tissues. However, rapid advances in human microbiome research involve collecting large amounts of data on microorganisms that exist in symbiotic relationships with the human body. Currently it is not clear whether these microorganisms should be considered part of or separate from the human body. Arguments can be made for both, but ultimately it seems that the dichotomy of human versus non-human and self versus non-self inevitably breaks down in this context. This situation has the potential to add further complications to debates on biobanking. SUMMARY: In this paper, we revisit some of the core problem areas of privacy, consent, ownership, return of results, governance, and benefit sharing, and consider how they might be impacted upon by human microbiome research. Some of the issues discussed also have relevance to other forms of microbial research. Discussion of these themes is guided by conceptual analysis of microbiome research and interviews with leading Canadian scientists in the field.
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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 | 0.077 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.023 | 0.102 |
| Scholarly communication | 0.029 | 0.038 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".