Biobanks in Oral Health: Promises and Implications of Post-Neoliberal Science and Innovation
Bibliographic record
Abstract
While biobanks are established explicitly as scientific infrastructures, they are de facto political-economic ones too. Many biobanks, particularly population-based biobanks, are framed under the rubric of the bio-economy as national political-economic assets that benefit domestic business, while national populations are framed as a natural resource whose genomics, proteomics, and related biological material and national health data can be exploited. We outline how many biobanks epitomize this 'neoliberal' form of science and innovation in which research is driven by market priorities (e.g., profit, shareholder value) underpinned by state or government policies. As both scientific and political-economic infrastructures, biobanks end up entangled in an array of problems associated with market-driven science and innovation. These include: profit trumping other considerations; rentiership trumping entrepreneurship; and applied research trumping basic research. As a result, there has been a push behind new forms of 'post-neoliberal' science and innovation strategies based on principles of openness and collaboration, especially in relation to biobanks. The proliferation of biobanks and the putative transition in both scientific practice and political economy from neoliberalism to post-neoliberalism demands fresh social scientific analyses, particularly as biobanks become further established in fields such as oral health and personalized dentistry. To the best of our knowledge, this is the first analysis of biobanks with a view to what we can anticipate from biobanks and distributed post-genomics global science in the current era of oral health biomarkers.
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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 | 0.073 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.030 | 0.039 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".