Bibliographic record
Abstract
The Conference Board of Canada created theLeaders' Roundtable on Commercialization to establish a sharedcommercialization vision for Canada and an action plan that recognizes theunique challenges facing various sectors and regions.Composed of47senior business executives, university presidents and deputy ministers, thisroundtable conducted 35 interviews with scholars, firm executives, andgovernment officers to gain insight into tactical actions (quickhits) that can be taken within the next 12 months to address problems andseize opportunities in commercialization. These actions are the subject of this report.The quick hits arecategorized into people, research, financing, and institutions.The quickhits identified include the following: (1) industry-led collaborative researchnetworks, (2) regionally-based commercialization internships, (3) angel taxcredits, (4) pilot program to expand R&D tax credits, (5) strategicprocurement, and (6) federal seed capital investment. For each of the quick hits, anexplanation of the action, a rationale,and how it will be measured are provided.An integrated, long-termstrategy has yet to be developed, but these quick hits provide a foundation forthe work of this roundtable. (SRD)
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 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".