Bibliographic record
Abstract
Shifting geopolitics, regional conflicts, climate change, and technology shocks: these are just some of the factors that will make the twenty-first century dangerous for Canada. Adaptability, the capacity to anticipate and manage dangers, is essential for the country to survive and thrive. But Canada is not as adaptable as it once was. In The Adaptable Country Alasdair Roberts explains what this vital ability means and why we are currently falling short. Politicians, he argues, are overloaded and fixated on the next election. Governments no longer launch big projects to think about the future. Leaders have stopped meeting regularly to discuss national priorities. Technological changes have undermined journalism and the ability of citizens to talk civilly about public affairs. The public service has become less agile because of a decades-long buildup of controls and watchdogs. While in many ways Canada is a better country than it was a generation ago, it is also more complex and harder to govern. The Adaptable Country outlines straightforward reforms to improve adaptability and reminds us about the bigger picture: in a turbulent world, authoritarian rule is a tempting path to security. Canada’s challenge is to show how political systems built to respect diversity and human rights can also respond nimbly to existential threats.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".