The Importance of Entry to Canadian Manufacturing with an Appendix on Measurement Issues
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
Understanding the importance of the dynamic entry process in the Canadian economy involves measuring size of entry. The main purpose of this paper is to summarize the information we have on the amount of entry in Canada. The paper also fulfils another purpose. Some studies have focused on cross-country comparisons (Geroski and Schwalbach 1991; OECD 2001). Interpretation of the results of these studies is difficult unless methodological issues regarding how entry is measured are addressed. Without an understanding of the extent to which different databases produce different results, international comparisons are difficult to evaluate. Cross-country comparisons that are derived from extremely different data sources may be misleading because of the lack of comparability. Since there is more than one reliable database that can be used to estimate entry in Canada, this paper asks how measured entry rates vary across different Canadian databases. By examining the difference in entry rates produced by these databases, we provide an estimate of the range or confidence interval that should be used in evaluating whether there are real differences in measured entry rates across countries. We also offer guidance as to the questions that should be asked about the databases used by researchers who conduct international studies. Finally, we make suggestions as to areas of comparison on which international studies should focus.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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