The “lumpiness” thesis revisited: the venues of policy work and the distribution of analytical techniques in Canada
Bibliographic record
Abstract
This chapter contributes to the understanding of analytical practices and tools employed by policy analysts involved in policy formulation and appraisal by examining data drawn from 15 surveys of federal, provincial and territorial government policy analysts in Canada conducted in 2009-2010, two surveys of NGO analysts conducted in 2010-2011 and two surveys of external policy consultants conducted in 2012-2013. Data from these surveys allows the exploration of several facets of the use of analytical tools, ranging from more precise description of the frequency of use of specific kinds of tools and techniques in government to their distribution between permanent government officials and external policy analysts. As the chapter shows, the frequency of use of major types of analytical techniques used in policy formulation is not the same between the three types of actors and also varies within government by Department and issue type. Nevertheless some general patterns in the use of policy appraisal tools in government can be discerned, with all groups employing process- related tools more frequently than ‘substantive’ tools related to the technical analysis of policy proposals.
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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.036 |
| Science and technology studies | 0.026 | 0.031 |
| Scholarly communication | 0.029 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".