Using web content analysis to create innovation indicators—What do we really measure?
Bibliographic record
Abstract
This study explores the use of web content analysis to build innovation indicators from the complete texts of 79 corporate websites of Canadian nanotechnology and advanced materials firms. Indicators of four core concepts (R&D, IP protection, collaboration, and external financing) of the innovation process were built using keywords frequency analysis. These web-based indicators were validated using several indicators built from a classic questionnaire-based survey with the following methods: correlation analysis, multitraits multimethods (MTMM) matrices, and confirmatory factor analysis (CFA). The results suggest that formative indices built with the questionnaire and web-based indicators measure the same concept, which is not the case when considering the items from the questionnaire separately. Web-based indicators can act either as complements to direct measures or as substitutes for broader measures, notably the importance of R&D and the importance of IP protection, which are normally measured using conventional methods, such as government administrative data or questionnaire-based surveys.
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 | 0.018 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 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".