Chronicling public sector renewal in Canada:
Bibliographic record
Abstract
“to err is human, to blame is politics” The IPAC Award for Innovative Management was launched in 1990. The stated purposes of the Award were as follows: • To enhance the image of the public sector; • To recognize organizations and individuals for creative and effective ways of doing things; • To identify and publicize success stories in the public sector worthy of emulation; and • To foster innovation. In the mid-eighties in Canada there was growing dissatisfaction with government and the public sector as taxes and deficits continued to rise. The private sector had downsized: government had not. The private sector had modernized service delivery to its customers; government appeared uninterested in serving the citizens better. Government was perceived as being bureaucratic in the worst sense of the word and risk adverse, fearing that any mistakes would lead to further attacks from politicians, journalists, the private sector and the general public. However, the Institute of Public Administration of Canada (IPAC) knew that there were many exciting changes taking place in the public service of Canada but there was only anecdotal information. An Award for Innovative Management might just bring these changes to light and encourage others to do things differently.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.049 | 0.020 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".