Measuring the Impact of Research on Well-being: A Survey of Indicators of Well-being
Bibliographic record
Abstract
The main objective of this report is to conduct a survey and assessment of various indicators used by organizations, both in Canada and abroad, to measure attributes and the well-being of society at the economic, health, environmental, social, and cultural levels. The compilation includes a combination of quantitative and qualitative and objective and subjective indicators or measures. The report is divided into five major parts. The first part provides a brief overview of Canada's research effort. The second part, by far the longest section, surveys a large number of sets of indicators and composite measures that have been developed to quantify well-being in Canada, in the United States, in OECD countries, and at the international level. The third section develops a preliminary framework for measuring the impact of research on well-being. The fourth section discusses briefly the role of indicators in public policy initiatives to improve the well-being of Canadians. The fifth and final section outlines directions for further work. The report concludes that it is entirely feasible to assess the impact of research investments in Canada on various dimensions of well-being. But it is important to specify what particular research investments and what dimensions of well-being are of interest given the many types of research investments and well-being dimensions as well as the complex interrelationships between research and well-being.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".