Rural 2.0?: Investigating Place-Based Rural Innovation Systems & their Implications for Public Policy & Community Development Practice
Bibliographic record
Abstract
We live in an era obsessed with innovation. So much so that in 2016, the Government of Canadabegan work on a new national‘Innovation Agenda’ with the following proposition: “Innovationis a Canadian value. It’s in our nature, and now more than ever, it will create jobs, drive growthand improve the lives of all Canadians. It’s how we make our living, compete and providesolutions to the world. We have the talent, the drive, the dedication and the opportunity tosucceed. So, what’s next?” However, as every public consultation on the Innovation Agenda tookplace in a major city and produced initiatives with names like ‘the Smart Cities Challenge,’ itseems like ‘what’s next’ is a national innovation conversation so steeped in unquestioned urbanism that it fails to even acknowledge, let alone include, rural Canadians. This doctoralresearch project will use a comparative case study approach to investigate the complexrelationships at play in place-based rural innovation systems and provide grounded, illustrativenarratives for informing public policy.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".