Validation of a web mining technique to measure innovation in the Canadian nanotechnology-related community
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
[EN] In this exploratory study, we explore a methodology using a web mining technique to source data in order to analyse innovation and commercialisation processes in Canadian nanotechnology firms. 79 websites have been extracted and analysed based on keywords related to 4 core concepts (R&D, intellectual property, collaboration and external financing) especially important for the commercialisation of nanotechnology. To validate our methodology, we compare our web mining results with those from a classic questionnaire-based survey. Our results show a correlation between the indicators from the two methods of r=0.306 (p-value=0.007) for R&D, of r=0.368 (p-value=0.002) for IP, of r=0.222 (p-value of 0.071) for Collaboration and of r=0.222 (p-value=0.067) for external financing. We conclude that some of the data extracted by our web mining technique can be used as proxy for specific variables obtained from more classical methods.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.035 | 0.109 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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 it