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A Community Facilitation Model for E-Government

2008· book-chapter· en· W4240090899 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueElectronic Government · 2008
Typebook-chapter
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsWestern University
Fundersnot available
KeywordsGovernment (linguistics)FacilitationEmpowermentQuality (philosophy)Action (physics)PopulationBusinessSPARK (programming language)Knowledge managementPublic relationsProcess managementEnvironmental planningComputer scienceManagement sciencePolitical scienceEngineeringEconomic growthEconomicsGeographySociology

Abstract

fetched live from OpenAlex

This chapter introduces a community facilitation model for e-government. The central tenet of this approach is the empowerment of a segment of the population to act, by providing the tools and information necessary to tackle issues that have been difficult to address with traditional approaches to government. Under this model, government provides an initial spark and then plays a supporting role in the growth of the community. By doing so, the costs of the program are minimized while the impact of the program is maximized. We examine the viability of the model by looking at a case study in water quality monitoring. The case illustrates the power of a government facilitated community of action to address an important problem, and it suggests that such a model can be applied globally and may be relevant to government initiatives beyond water monitoring.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.901
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.039
GPT teacher head0.273
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it