It’s All About People – Really! Some Human
Bibliographic record
Abstract
The world was facing myriad economic and political challenges at the end of 2014, and some of the new year’s early headlines have indeed been grim. Amid these challenges, Canada’s relative attractiveness in a troubled world stands out as clearly in early 2015 as it has ever done. In considering how we got here, and how we could improve our position, I keep coming back to the familiar phrase “it’s all about people ” – the idea that people are the most vital resource for an organization, for a community, and for a country. Whatever 2015 may bring – and volatile resource prices, economic turmoil in Europe and elsewhere, not to mention episodic violence around the globe, guarantee a host of challenges – we will do better if we focus resolutely on developing and unleashing the full potential of Canada’s human capital. The Global View, and Some Numbers One pleasure of working at the C.D. Howe Institute is the opportunity it provides for me to talk with Canadian leaders in multinational organizations. And my job makes it natural to ask them how they pitch Canada as a place to invest and operate to their colleagues elsewhere. Time and again, the first thing they mention relates to our people. The workforce might be top-of-mind; or the professional services; or the quality and integrity of public officials. Increasingly, they may mean our openness and diversity and ability to get along with each other. Whatever the specifics, the quality of Canadians heads their list. Leaders with a global view know that not all countries are so favoured. Yes, many places have milder winters. Rich natural resources are not a Canadian monopoly. Increasingly, capital and technology are available worldwide. But Canada has done more and better with its natural resources, capital, and technology than most – in large part because it has developed and attracted able people.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.050 | 0.024 |
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".