Bibliographic record
Abstract
A patent is a legal right granted by a patent authority that gives the inventor or assignee exclusive rights to produce, use, sell and distribute an invention for a specified period of time. Patent acquisition offers numerous advantages related to the protection of inventions, enhancement of competitiveness, attraction of investors, generation of income, support for innovation, market expansion, and improvement of brand reputation for the individual or entity that holds the patent. Given the significance of the patent concept, the performance of the G7 countries regarding patent acquisition is handled as multi-criteria decision-making (MCDM) problem in this research. In the evaluation made over the total number of patent applications submitted over nine different technological domains between 2000-2020, the criteria weights were determined by MAXimum of Criterion (MaxC), Modified Preference Selection Index (MPSI) and LOgarithmic Percentage Change-driven Objective Weighting (LOPCOW) methods. By using these criteria weights which are combined with the Bonferroni Mean Operator, the MUltiple-TRIangles ScenarioS (MUTRISS) method was utilised for the performance rankings of G7 countries in terms of patent acquisition. As a result of the study, the success ranking of G7 countries in terms of patent applications was determined as USA, Japan, Germany, UK, France, Canada and Italy.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".