Bibliographic record
Abstract
There has been an explosion of interest in recent years in Canada and other countries in macro-indicators and composite indexes of economic and social well-being. This reflects growing recognition of the important role macro-indicators can play as a tool for evaluating trends in and levels of economic and social development and for assessing the impact of policy on well-being. This report provides a literature review of conceptual/operational frameworks for the development of macro-indicators that give an assessment of economic, labour market and social conditions or states of well-being. The report provides an analysis of frameworks for macro-indicators by discussing general framework issues; identifies and describes six specific frameworks for macro-indicators which the author regards as particularly important or relevant, and discusses the strengths and weaknesses of these sets of indicators/composite indexes; and provides a description of an additional 31 sets of indicators and composite indexes broken down into economic, social, economic/social, and labour market areas. The report concludes that no existing framework currently includes all important concepts and linkages and that it is unlikely that one ever will. As the survey of the macro-indicators literature reveals, the development of a framework for macro-indicators involves choices related to the domains of interest, the purpose for which the indicator is designed, and the population to be covered, among others. Choices or tradeoffs must be made and a balance struck between conceptual sophistication and transparency and between complex linkages that could potentially confuse the user and simplicity.
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 | 0.038 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.049 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".