Yellow Pages Global Entrepreneurship Monitor Australia 2000
Bibliographic record
Abstract
The Global Entrepreneurship Monitor (GEM) refers to both a set of linked, international research projects and a set of documents that reports project results. Each year, a number of countries (10 last year, 21 this year and growing) perform related entrepreneurship research using identical methods. They each produce an independent report (GEM Australia, GEM USA, GEM Japan et cetera) which explores in considerable detail the nature, extent and effects of entrepreneurship within their individual country, including selected comparisons with other nations. Additionally, one international, coordinating document (the GEM Executive Report) is produced. It summarises each nation’s findings and discusses them at the level of international generality. GEM was conceived in September 1997 as a joint research initiative by Babson College (USA) and London Business School. It went ‘into the field’ for the first time last year. The central aim was, and is, to bring together the world's best scholars in entrepreneurship to study the complex relationship between entrepreneurship and economic growth. From the outset, the project was designed to be a long-term multinational enterprise. In order to obtain reliable, comparable data, GEM originally focused on the G7 countries (Canada, France, Germany, Italy, Japan, United Kingdom (UK) and USA), with three additional countries (Denmark, Finland and Israel) added because of the availability of scholars in these countries with particularly relevant expertise. GEM 2000 extends coverage to 21 countries in total. The additions are Argentina, Australia, Belgium, Brazil, India, Ireland, Norway, Singapore, Spain, South Korea and Sweden. Eventually, it is envisaged 40 to 50 countries will be included.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.136 | 0.083 |
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".