INNOVATION IMPACTS: MEASUREMENT AND ASSESSMENT The Expert Panel on the Socio-economic Impacts of Innovation Investments ii Innovation Impacts: Measurement and Assessment THE COUNCIL OF CANADIAN ACADEMIES
Bibliographic record
Abstract
(CAE), and the Canadian Academy of Health Sciences (CAHS), as well as from the general public. The members of the expert panel responsible for the report were selected by the Council for their special competencies and with regard for appropriate balance. This report was prepared in response to a request from the Ontario Ministry of Research and Innovation. Any opinions, findings, or conclusions expressed in this publication are those of the authors, the Expert Panel on the Socio-economic Impacts of Innovation Investments, and do not necessarily represent the views of their organizations of affiliation or employment. Library and Archives Canada Cataloguing in Publication Innovation impacts: measurement and assessment [electronic resource]: socio-economic impacts of innovation investments of the government of Ontario/ Council of Canadian Academies. Includes bibliographical references and index. Electronic monograph in PDF format. Issued also in print format. ISBN 978-1-926558-58-5 1. Public investments – Ontario – Measurement. 2. Public investments –
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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 | 0.049 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.022 | 0.035 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".