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Record W2409611803 · doi:10.1145/2910019.2910047

Applying a Knowledge Audit Strategy for Public Corporate Boards

2016· article· en· W2409611803 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInformation Architecture and Usability
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAuditKnowledge managementContext (archaeology)Tacit knowledgeGovernment (linguistics)Set (abstract data type)BusinessAction (physics)Computer scienceAccounting

Abstract

fetched live from OpenAlex

Government boards are being asked to provide more oversight in creating and protecting public value. Much attention has been focused on the implementation of policies, processes and structures aimed at assisting directors with their new role. However, several surveys are reporting that they still do not have the necessary information and knowledge. While many studies have provided valuable insights into information-related issues, there remains a strong need for comprehensive guidelines that can help to determine specific information needs, problems, and solutions. The objective of this paper is to present the roadmap for an action research project that is set to start in 2016. It intends to validate the use of knowledge audit methodology in the context of public boards. The framework should help public organizations and their boards to determine a knowledge strategy that capitalizes on both tacit and explicit knowledge and on communication technologies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

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

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 head0.077
GPT teacher head0.284
Teacher spread0.207 · 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

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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