Using Discourse Genres for Knowledge-Building Activity in a Government Organization
Bibliographic record
Abstract
Building knowledge in professional organizations involves complex discursive practices.In 2014 a group of employees at the Public Health Agency of Canada (PHAC), including senior managers, project facilitators, and other staff members, collaborated in managing a communication problem involving PHAC's scientists and policy writers, an effort known as the Science to Policy Project.This study investigates how an activity system, with its genre set, was used to build knowledge regarding the causes of the problem and also possible solutions.As well, the study looks at key genres from the government-wide genre system in which this activity of knowledge-building was situated.At the same time, the study describes PHAC's attempt to implement a new organizational culture to facilitate the knowledge-building activity the employees were engaged in.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".