Bibliographic record
Abstract
Government funding for science has been under threat. Corporations and private industries (who already make up the majority of research and development funding in Canada) are often the favourites to make up the deficit when public funding gets axed. However, private funding for science can introduce conflicts of interest, biases, and profit-motivated agendas, while public funding is proven to result in impartial, ethical, and people-first science full of long-term benefits. This article explores the implications of corporate funding on scientific integrity by examining the checks and balances of the public funding system, examples of funding bias, and the dangers of disinformation. It becomes clear that candid funding disclosures, transparency of research initiatives, and standards to regulate commercial-science interactions are needed to preserve scientific integrity, for our own good.
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 | 0.069 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.021 | 0.042 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.011 | 0.001 |
| Research integrity | 0.001 | 0.016 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".