The 3 Bs of Impact Assessment of Technology Transfer Programs: Rationale, Technique, and a Case Example from the Canada Centre for Remote Sensing
Bibliographic record
Abstract
Impact assessment reviews have become increasingly popular in both the public and the private sector: in the private sector, to develop valid and reliable ways to determine where R & D funds should be invested; in the public sector, in order to respond to public pressures for responsible use and reporting on the expenditure of public funds. The study reviews commonly-used methodologies to assess RD (2) believability of procedures and outcomes (based on well-researched information and a good reporting process), and (3) buyers of the process and results (dependent on buy-in and believability, as well as good application, dissemination, and promotion). An application is described in detail, in an impact assessment by a government unit in Canada – CCRS, or Canada Centre for Remote Sensing. Data collection involved a combination of methods, both qualitative and quantitative: industry statistics, data collection from CCRS project leaders, and interviews with the client partners. The results show that CCRS has made a groundbreaking effort in implementing a successful impact assessment process which achieves buy-in, believability, and buyers. It is recommended that systems be developed that make the impact assessment process easier, cheaper, and a more regular activity. Also, information systems which create up-to-date and complete project files are important.Concludes that, in order to facilitate and regularize the impact assessment process, collaborative agreements between government departments and external clients should include expectations for regular reporting of outputs, outcomes, and impacts. Similarly, a wide dissemination of information about experiences in impact assessment and cross-departmental committees can help to build a rigorous and adaptable approach. (CBS)
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".