Do Large Firms Pursue More Process Innovation? A Case of Canadian Manufacturing Industries
Bibliographic record
Abstract
We test the Cohen & Klepper cost-spreading process share hypotheses using unique data from two national innovation surveys (2009 and 2012). To our knowledge, no other study has the same combination as our dataset, in terms of robust data from a mandatory survey, large sample size, diverse measures for innovation output, and no sample selection bias. We use two direct measures of innovation to test the CK hypothesis: R&D expenditure and the number of innovations. An outcome variable that counts the number of innovations can be easier for respondents to recall from memory and they may reflect the firm's activities more accurately. Using direct measures of innovation eliminates three forms of bias emanating from patents. Our results show that the CK hypotheses can be supported with the aggregated sample, but the results are weak for separate industries. The count-based process share provides statistically superior results to the expenditure-based process share.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".