Bibliographic record
Abstract
What is the future of policymaking in Canada?It is tempting to suggest, as the saying goes, that the future will resemble the past, but even more so.That is, we might suppose that the policy challenges of the next fifty years will be more or less the same as the ones of the last half-century, but weighed down by the accretions of decades of wrangling.A glance at the table of contents of this volume reinforces that view.The core topics -including immigration and multiculturalism, federalprovincial relationships, and aboriginal governance -have been the meat and potatoes of Canadian governance for decades now, and the analyses offered here give every indication that the policy fights in these arenas are far from settled.But if you scan the headlines or your social media feed, you will get a much different sense of the agenda.Dealing with the products of Silicon Valley alone will be enough to occupy policymakers for a generation -from the platforms (Facebook, Google, Uber, Amazon) to CRISPr to drones to the privatization and commercialization of space to autonomous cars -and every week seems to bring a new technology or new development that threatens to overturn the foundations of the social and economic order.But even if we set aside the incessant and more or less unpredictable technological evolution that colours every assumption about what the future will be like, the policy landscape of the next fifty years is looking, if not radically changed, at least tilted in a different direction from what we have faced in the past.It is starting to look as though the assumptions that have guided policymaking in the past, and the solutions we have developed to our biggest challenges, will be more hindrance than help in the future.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.030 | 0.013 |
| Scholarly communication | 0.029 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".