Validation of a web mining technique to measure innovation in the Canadian nanotechnology-related community
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.
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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".