Local \nGovernance, \nCreativity \nand \nRegional \nDevelopment \nin \nNewfoundland \nand \nLabrador: \nLessons \nfor \nPolicy \nand \nPractice \nfrom \nTwo \nProjects
Bibliographic record
Abstract
This report is based on findings from two significant research projects presented at the Celtic \nRendezvous Workshop from June 10-12th, 2010. The first project, Rural-Urban Interaction in \nNewfoundland and Labrador: Understanding and Managing Functional Regions considers regional \nlabour market development, governance and the need for planning to be based on ‘functional’ rather \nthan simply ‘administrative’ regions. The second project, the Innovation Systems Research Network \n(ISRN), is part of a $2.5 million Social Sciences and Humanities Research Council of Canada – \nMajor Collaborative Research Initiative (SSHRC – MCRI) exploring the social dynamics of \neconomic performance in fifteen city regions across Canada. This research, led nationally by David \nWolfe at the University of Toronto, has three major themes: (1) the social dynamics of innovation; \n(2) talent attraction and retention; (3) and governance and inclusion. \n \nDay One of the workshop included presentations on the Functional Regions Project by Alvin \nSimms and Kelly Vodden and presentations on the ISRN Project by Greg Spencer, Anne-Marie \nVaughan, Rob Greenwood, Ken Carter and Damian Creighton, with time set aside for lively debates \nand discussions. The following day started with a panel discussion on the insights and lessons from \nday one, including Bruce Gilbert, Sheila Downer, Kevin Morgan, and Susan Drodge. This was \nfollowed by break-out groups examining the key lessons from this research for policy and practice in \nNewfoundland and Labrador from the perspective of industry, municipal government, the federal \nand provincial governments, and NGOs. The workshop concluded with a five-member panel \ndiscussion on the implications of these findings involving Bill Reimer, Craig Pollett, Richard \nShearmur, Lisa Browne and Kevin Morgan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.082 | 0.011 |
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".