Are innovative regions more unequal? A case study of Canadian regions, 1981-2016
Bibliographic record
Abstract
Since the 1980s, Canada, like many other OECD countries, has experienced a significant increase in income inequality. Persistent growth in disparities between the wealthy few and the rest has led to concerns over growing resentment and populist discontent. In this fraught era, understanding the drivers of growing inequality is more critical than ever. One such proposed driver, gaining increasing traction in the literature, has been innovation. Long touted as the key to economic growth, recent scholarly discussion has turned a critical eye to innovation-driven growth, highlighting the ways in which technological changes can displace or devalue certain workers whilst rewarding others, worsening income inequalities. Using high-resolution patent data from the United States Patents and Trademark Office in addition to microdata from Canada’s Census of Population, this thesis empirically assesses the relationship between innovation and inequality at a pan-Canadian regional level using a series of spatial panel models. The descriptive results of the analysis reveal persistent divergent trends in both income inequality and innovation with urban regions both growing more unequal than rural regions and innovating at a faster pace than rural regions. The model results suggest that innovation is indeed positively and significantly correlated with inequality. This relationship appears to be further strengthened when innovation is restricted only to high-tech industries. While pro-creativity and innovation policies have been enormously popular in contemporary urban-economic discourse as a means to economic growth, the results suggest that such models need to be critically revisited and carefully implemented given the potential for innovation to induce inequality
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".