The policy worker and the professor: understanding how New Zealand policy workers utilise academic research
Bibliographic record
Abstract
How do policy workers actually use academic research and advice? While there are several recent studies regarding this question from other Westminster jurisdictions (e.g. Talbot and Talbot, 2014, for the UK; Head et al., 2014, for Australia; Amara, Ouimet and Landry, 2004 and Ouimet et al., 2010, Canada), similar academic studies have been rare in New Zealand. So far, most of the local research in this field has been conducted by the prime minister’s chief science advisor and the Office of the Prime Minister’s Science Advisory Committee, with the particular instrumental purpose of improving the government’s ministries and agencies’ ‘use of evidence in both the formation and evaluation of policy’. However, none of these studies have asked how, and to what extent, policy workers in government are utilising academic research in their everyday work.
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 | 0.101 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.016 | 0.053 |
| Scholarly communication | 0.039 | 0.034 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".