ASSESSING TECHNICAL EFFICIENCY OF INNOVATIONS IN CANADA: THE GLOBAL SNAPSHOT
Bibliographic record
Abstract
This paper presents the results of a comparison of the technical efficiency of innovation approach in Canada to approaches in 41 other countries. Data Envelopment Analysis was used to investigate this subject. Results of simulation experiments were used to anticipate possible general suggestions regarding policy measures that may be considered when exploring means to improve Canadian performance. Data from the World Competitiveness Yearbook and European Innovation Scoreboard were used. Oslo Manual definition of innovations was used. Enablers (context) — difficult to change country characteristics that may impact upon technical efficiency — were entered into the examination. A qualitative overview of the Canadian perspective to innovations supplements the quantitative portion of the presentation. It is observed that return to scale and congestion issues dominate considerations on technical efficiency of innovations. Wealthier countries seem to be less technically efficient in innovations than not so rich ones. Canada operates under Decreasing Returns to Scale. Congestions seem to be the main contributor to inefficiencies. Suggestions regarding the betterment of technical efficiency of innovations in Canada are presented here. Attention was drawn to several questions for further studies on the subject and their importance clarified.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".