International Research Handbook on Successful Women Entrepreneurs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Contents: 1. Introduction Sandra L. Fielden and Marilyn J. Davidson 2. Australia Glenice Wood 3. Brazil Andrea E. Smith- Hunter 4. Canada Karen D. Hughes 5. China Jonathan M. Scott, Javed Hussain, Richard T. Harrison and Cindy Millman 6. Denmark Suna Lowe Nielsen, Kim Klyver and Majbritt Rostgaard Evald 7. Fiji Gurmeet Singh, Raghuvar Dutt Pathak and Rafia Naz 8. India Tanuja Agarwala 9. Lebanon Dima Jamali and Yusuf Sidani 10. New Zealand Marianne Tremaine and Kate Lewis 11. Pakistan Jawad Syed 12. Portugal Christina Reis 13. Russia Anna Shuvalova 14. South Africa Babita Mathur-Helm 15. Turkey Mine Karatas-OEzkan, Goezde Inal and Mustafa F. OEzbilgin 16. United Arab Emirates Nnamdi O. Madichie 17. United Kingdom Susan Marlow and Maura McAdam 18. United States of America Mary C. Mattis and Leslie Levin Index
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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 it