Taming enterprise dementia in public sector organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The aim of this paper is to report the finding of an exploratory research project that considered how public service organizations may conquer the debilitating effects of enterprise dementia. Design/methodology/approach Building on the seminal research of Michael Earl, this project sought to solicit the view from the front, which in this case are the middle managers of the Canadian public service. Specifically, the aim was to determine which of Earl's schools of knowledge would be most appropriate in curbing the organizational memory loss and taming the information anxiety that are common place today. Findings The sample of public service middle managers overwhelmingly opted for a single strategy. The organizational school surfaced as the strategy most likely to fit respondents' perceived needs. Through collaboration, Earl's organizational school focuses on maximizing the use of social networks with a view to knowledge sharing. Practical implications This paper provides a compendium of knowledge strategies that may be useful for public service executives. Originality/value This the first project to consider how Earl's taxonomy of knowledge strategies may be implemented in a Canadian public service environment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it