The bonds of institutional language: A discursive institutionalist approach to the <scp>C</scp>lerk of the <scp>P</scp>rivy <scp>C</scp>ouncil's annual report
Bibliographic record
Abstract
Abstract The Clerk of the Privy Council's annual report is seen as an important documentation of the public sector's priorities, but has never been examined. Using a combination of traditional content analysis and digital humanities techniques, this study analyses the first sixteen reports written under the aegis of six clerks, and concludes that they reflect competing priorities and emphases. Yet while the reports are each distinct, they collectively are highly repetitive and remarkably unaffected by either the context in which they were written or the policies pursued by governments. The consistency of the texts over sixteen years of transformation in government indicates that such institutional discourse is slow to change, demonstrating a strong “bond” to what had been reported previously. This article also shows how a mix of methods in dissecting the use of language can contribute to the budding field of discursive institutionalism.
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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 | 0.033 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.019 | 0.087 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".