The Discursive Boundary Work of Recontextualizing Science for Policy: Opening the Black Box of an Organization’s Genre System and Intermediary Genre Sets
Bibliographic record
Abstract
Governments the world over require scientific knowledge to inform policy makers’ decision-making processes. The recontextualization of this information for nonscientific audiences has received much attention, though it has primarily focused on publicly available texts. Little is known about the discursive nature of how science is transformed and repurposed and the confidential writing performed by boundary organizations that are working between science and policy. This ethnographic study explores the collaborative discursive activity involved in efforts by a boundary organization—the Council of Canadian Academies—to recontextualize science for policy makers. The analysis opens the discursive black box of the genre system and intermediary genre sets involved in one project, which led to the publication and distribution of the boundary object of an advisory report, Older Canadians on the Move. I claim that the discursive boundary work involves a complex genre system containing several sequential genred activities through which science is transformed and a boundary object created.
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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.032 | 0.118 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".