Bibliographic record
Abstract
The final goal of the macro appraisal model of Canada is that the figure of society of the day is made by the record. The goal is that we evaluate the meaning documentary have in the social structure of the day to produce records. The organization to be affected much the society is recognized as the organism, 'Government' and the function analysis of the government is emphasized in order to be the documentation. Therefore, it is essential in the macro appraisal model that OPI, the department to carry out the very important function is identified. Also the research of governance about interaction of country and citizen is studied. Based on these studies, the macro appraisal theory is made and it can be macro appraisal methodology that these theory is verified through records. the government of Canada have operated macro appraisal model reflected the figure of the society since 1990's, on adapting the change of the record production of modern. This model of Canada is systemized concretely and the government of Australia introduced the model benchmarking the Canada's. The England and the Holland is bringing up the result of the project introduced the macro appraisal method. Therefore the macro appraisal is the method and the theory noticed globally in the field of appraisal of modern. Throughout the analysis of the macro appraisal system of Canada, how to select the record reflected the figure of the society is proposed, and concrete practice is also present, and concrete practice is also present. Also the improvement method of the appraisal system in Korea is considered.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".