Research and technology: Market-driven innovation in the twenty-first century
Bibliographic record
Abstract
In the twentieth century, the forest products industry evolved through three distinct focal orientations: a forestry orientation, a production orientation, and a marketing orientation. In each case, research and technology (R&T) was applied as a means of either solving the limitations associated with each orientation or shifting the industry orientation to the next focal point. In the beginning of the twenty-first century, R&T is required to facilitate a new shift for the wood products sector from a marketing orientation to a knowledge orientation. This requires an expansion of traditional research and technology to incorporate a market-based social sciences approach, along with the traditional physical and engineering sciences, as more than just an afterthought. In order to ensure future successes, innovative technological solutions must be applied to emerging market-based knowledge clusters such as connectivity, supply chain management, eBusiness, mass customization, and knowledge-based products. These are all practical manifestations of the new knowledge orientation. Each will require innovative R&T solutions to recreate successful wood products companies operating in the new millennium. Key words: marketing, research, technology, innovation, knowledge
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".