Bibliographic record
Abstract
In this brief, I will explore this linkage in its many manifestations - the interface of science and policy - with the goal to deepen the understanding of the challenges we are dealing with, in particular as they relate to scientists working for and with governments. The description of this lay of the land starts with the theoretical concepts (the view from the “stratosphere”) and progressively moves towards practical aspects. It will be composed of (a) a description of the concepts underlying the science/policy interface, (b) the manifestation of the interface with a focus on broad functions within organizations, and (c) a simple classification of the diverse uses of government science and, thus, locations where the science/policy interface may have to be managed. As I move from the theoretical to the practical, I also move from observations that are applicable to any organizational context to those that are most applicable to the situation in the federal government of Canada. I am attempting, however, to provide a map rather than directions at all times - an analytic taxonomy rather than an argument.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".