Bibliographic record
Abstract
Introduction Almost 40 years ago Peter deLeon, editor of the journal Policy Sciences , made the following observation: Throughout the government and private sectors, one hardly finds any office that does not have a staff ‘policy analyst’. Newly graduated baccalaureates engrave that title on their business cards and many senior government officials view themselves primarily as analysts…Clearly, policy analysis can be seen as a growth stock. Yet the pervasiveness of the genre leads one to question the heritage, present condition, and future of the discipline and the profession. (deLeon, 1981, p. 1) Most of what deLeon wrote in his 1981 editorial remains true today. Although the number of people whose business cards proclaim them to be policy analysts is very difficult to determine, it is conceivable that in both Canada and the United States their numbers approach those for physicians or lawyers. The number of policy analysts has surely grown quite significantly since deLeon described policy analysis as a “growth stock”. However, the strong hint of scepticism that creeps into his conclusion is not entirely fair. I argue that the policy analysis profession is at least as influential as deLeon and other leaders of what was known as the policy sciences movement hoped it would become, but in ways that they did not expect and that probably would have disappointed them. Even the approximate size of the policy analysis community in Canada is unknown (Howlett, 2009). In this respect, it is quite different from the medical and legal professions which have about 80,000 (CMA, 2017) and 95,000 (FLSC, 2014) members, respectively. Unlike these professions and such others as accountants, engineers, teachers, and nurses, there is no required certification before one can be recognized by others as a policy analyst. This, of course, has to do with the fact that the policy analysis profession is not linked to any particular discipline. Someone whose business card proclaims him or her to be a policy analyst may have training in economics, criminology, public health, women's studies, international security studies or any number of disciplinary backgrounds, some of which are by their very nature multidisciplinary.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".