Bibliographic record
Abstract
PURPOSE: Many industries are affected by the stale global economy recently. However, the innovative pharmaceutical and biotechnology industry seems to be relentlessly producing a wealth of newer therapeutic products to meet the increasing demand of the aging population. These companies" abilities to produce safer and more effective products are the results of research and development (R&D) spending. Clearly, by investigating R&D expenditures of patentees, one can paint a better picture of the condition in this sector of the Canadian economy. METHODS: To track patentees R&D expenditures, the Patented Medicine Prices Review Board (PMPRB) annual reports have become the primary sources of data. Furthermore, the financial market and the Therapeutic Directorate Annual Drug Submission Performance Report have also been used to address some of the patterns that are seen in PMPRB data. RESULTS: Most of the data suggest excellent growth in the innovative pharmaceutical and biotechnology sector in Canada. Despite this growth, it still lags behind the demand of newer therapeutic products. Nevertheless, the industry can weather a volatile economy well. CONCLUSIONS: Patentees R&D expenditure is a good indicator of the health in the industry. It provides a perspective not only within Canada itself, but also globally.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".