Technology: The classic Canadian dilemma—Short-term gain for long-term pain!
Bibliographic record
Abstract
In a globally competitive world, innovation is an essential component of long-term success. For commodity industries in particular, global companies will dominate, with niche-market, nimble, small companies providing specialized products to select customers. Both will require technology. To survive in international markets, Canadian pulp and paper producers must develop integrated business and technology strategies to meet global competition from low-cost fibres and state-of-the-art mills. For competitive positioning, and for increased returns on investment, the mandatory progress in cost reduction must be balanced with revenue growth through new product innovations. Companies can leverage their limited resources through participation in the programs of a research institute. Paprican, as an example, provides access to broadly based technical skills in areas related to cost reduction, and environmental sustainability. At the same time, it delivers world-class, strategically driven research that enables new product design and development. For technologies related to public policy directives such as environmental performance or global warming initiatives, governments must participate as stakeholders in the solutions to their issues. Key words: pulp and paper industry, international competitiveness, research and development, research institutes, innovation, return on investment, multidisciplinary research, public policy
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.034 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".