New directions for the biopharma industry in Canada: modelling and empirical findings
Bibliographic record
Abstract
Purpose The main purpose of this study is to identify features and trends shaping the business models currently prevailing in the Canadian biopharma industry, by disaggregating the business model analysis into four key areas: value creation, investment strategy, business strategy, success factors Design/methodology/approach Results arise from an empirical fieldwork of qualitative nature, undertaken by the end of 2004, involving deep interviews to a broad variety of key stakeholders of the biopharma industry in the Quebec region, including biopharma firms, large pharma firms, venture capital funds, research centers and recognized experts from consultancy firms and Universities. Findings Biopharma firms encounter difficulties to bridge the gap between the research innovation focus to the large scale production focus. The biopharma firms need to announce achievable and promising hits in the near future, mainly by filing patents and also through scientific publications, and making believable their prospects to reach the release phase. Venture capitalists and private investors claim for original, innovative and marketable results, keeping away from just imitative enterprises. The one product firms still largely prevail. Research limitations/implications Lessons learned through this fieldwork might in the future be complemented with a quantitative survey to biopharma firms in the Quebec region. Practical implications When funding biopharma firms, private investors claim for original, innovative and marketable results, keeping away from just imitative enterprises. The business model must evolve, and take into account the quick changes in the environment, coming either from the market or from the technologies and research streams. Originality/value Disaggregating the business model analysis into four key areas will make an original contribution to the limited knowledge about expectations and future prospects of the biopharma industry
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".