A Conceptual Framework to Guide Research on Private Sector Development in Developing Countries
Bibliographic record
Abstract
Documents made available through this series result from the work of IDRC’s program initiative on “Globalization, Growth, and Poverty ” (GGP). They are intended to contribute to the knowledge base and debate around critical issues on the inter-linkages among growth, poverty, inequality and globalization, and on the generation of capacities for evidence based policy making. Canada’s International Development Research Centre (IDRC) is one of the world’s leading institutions in the generation and application of new knowledge to meet the challenges of international development. To fulfill its mission of “Empowerment through Knowledge”, IDRC works in close collaboration with researchers from the developing world in their search for the means to build healthier, more equitable, and more prosperous societies. The papers in this series have been developed in the context of GGP’s work and include literature reviews, scoping studies and, occasionally, issue-oriented evaluation studies. They are written primarily by IDRC staff, hired consultants and interns. The papers are not subject to peer review, nor reviewed or edited for style and content. The views expressed are those of the author(s) and do not necessarily represent those of the International Development Research Centre or its Board of Governors. IDRC’s Working Papers on Globalization, Growth and Poverty are published and distributed primarily in electronic format via www.idrc.ca/ggp, though hardcopies are available upon request. Working papers may be copied freely for research and educational purposes and cited with due acknowledgment.
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 | 0.059 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.029 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".