The power of data or why scholars should pay attention to policy
Bibliographic record
Abstract
Abstract EDITOR'S SUMMARY The proposal to abandon Canada's long‐form census is one example of an alarming shift to cut production of and public access to authoritative scientific data, undermining formation of good public policy. This is contrary to official pronouncements since 1996 recognizing data and information technology as critical resources necessary to promote innovation, wealth, service delivery and global competitiveness. More ubiquitous technology and wider access to information have not translated into better quality of life and good government relations. National policy formation increasingly takes place without the benefit of valid information, in an environment where government transparency is blocked, information gathering is curtailed and access is restricted. From a political economy perspective, information serving capital accumulation is valued over that serving social welfare. Discussion of factors leading to information restrictions and the policy implications should be strongly encouraged among the populace, in academia and throughout social media.
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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 | 0.052 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.031 | 0.039 |
| Insufficient payload (model declined to judge) | 0.012 | 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".