Government department leads successful change challenge: national voluntary health organizations plan for future state
Bibliographic record
Abstract
{Paragraph 1 of Introduction]: The last decade of this century has been a difficult one for the public sector. Budgets have been cut, programs merged or eliminated, and operational staff downsized. There have been societal changes including shifts in attitudes and values, and excessive consumerism. During the decade, the 'victim' phenomena has become a prevailing force with well organized special interest groups demanding recompense for perceived injustices. And days of universal entitlements have been under critical review, if not coming to an end. All government levels have been under increased pressure for services, yet have had fewer resources with which to respond and a less clear mandate with which to work. Statements such as "I'm from the government and I'm here to help you," are met with wry smiles as many government programs' credibility has waned. New and creative responses are required to meet the challenges of responding to these 'new realities'. This article is a report of a government department that radically altered its service delivery and pioneered an experimental program based on the latest understandings from the field of organization development. Keywords: CVSS, Centre for Voluntary Sector Studies, Working Paper Series,TRSM, Ted Rogers School of Management Citation:
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.022 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".