Biobanks in Oral Health: Promises and Implications of Post-Neoliberal Science and Innovation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it