The Organizatonal Culture of Digital Government
Bibliographic record
Abstract
This chapter examines the characteristics of government organizations that influence their capacity to employ information technology (IT) in a strategic manner such that it assists them in their quest to meet governance challenges. We explore the organizational factors, architectural and cultural, that impede large government departments from moving beyond the adoption of IT as a mere instrument that assists the execution of routine tasks in the traditional way and move into new forms of governance that alter the relationships between individuals and units within the organization and between the organization and its external environment. Our objective is to provide a useful framework for the analysis of the barriers to, and potential catalysts of, an IT mediated transformation of the governance of large government departments. Our insights are based on explorations of the issues surrounding the development of new governance models for data and informatics management within Fisheries and Oceans Canada, the federal department with a leading role in a wide range of activities relating to Canada’s marine environment. As one of the world’s leading marine science institutions, this case underscores the fact that technical competence alone is insufficient to facilitate a shift towards digital government. Using IT strategically is a governance challenge that is contingent upon organizational structure and culture. Science and engineering produce ‘know-how’; but ‘know-how’ is nothing by itself; it is a means without an end, a mere potentiality, an unfinished sentence. ‘Know-how’ is no more a culture than a piano is music. E.F. SchumacherRequest access from your librarian to read this chapter's full text.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".