Acknowledging the Gaps in Our Knowledge Economy: A Call for Clear Thinking on High Tech in Nova Scotia
Bibliographic record
Abstract
The 2003 edition of NovaKnowledge’s Nova Scotia Knowledge Economy Report Card understandably interprets the economic data in the most positive light possible, perhaps in an attempt to persuade readers outside the province that Nova Scotia’s knowledge economy is performing better than it is. Nova Scotians, however, might find a more objective analysis of economic indicators will make it easier to make the right decisions with respect to improving their knowledge economy performance. For example: • The Report Card states that “Nova Scotia’s economy pays an added premium to the well educated, in comparison with national standards.” In fact, average earnings for university graduates in Nova Scotia are 18 percent lower than for those in Canada. • The Report Card refers to outmigration of highly educated workers without pointing out how severe the problem really is: a net 15 percent for university graduates across all disciplines, and far higher in the pure and applied sciences; moreover, the situation is predicted to get worse. • The Report Card cites strong job creation, glossing over the highly significant weakness in the creation of jobs in industries with aboveaverage earnings, where Nova Scotia performs at 10 percent of the national average. • The Report Card states that “Nova Scotia’s service sector is relatively productive in a Canadian context,” even though the province lags the Canadian average in every category except one, by almost 20 percent across all industries and in one case by more than 50 percent. Open debate, objective measurement, and collective action on the challenges facing Nova Scotia’s economy are the best ways to contribute to positive action to meet those challenges. Acknowledging the Gaps in Our Knowledge Economy: A Call for Clear Thinking on High Tech in Nova Scotia
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".