Revisiting the Canadian public policy towards venture capital: Crowding-out or displacement
Bibliographic record
Abstract
Science and Public Policy, 2018, https://doi.org/10.1093/scipol/scy005 This article has been amended to add details not included in the version originally published online. The following note has been added: ‘The authors are open to providing the SQL and Stata code that generated the estimation data. Also, if requested, we can provide the raw data, however, these data are protected. The researcher would need to sign a Non-Disclosure Agreement before we could send them the data.’ The following has also been added to the Acknowledgements: ‘We wish to especially thank Professor James Brander from the Sauder School of Business, the University of British Columbia for reading the paper and providing helpful comments.’ The article has been updated online to reflect these changes.
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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".