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Record W2789885887 · doi:10.3138/cpp.2017-037

Internet Communications Technology Skills and Systems of Engagement

2018· article· en· W2789885887 on OpenAlexaffvenue
Trevor Deley, Marcellus Mindel

Bibliographic record

VenueCanadian Public Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsIBM (Canada)University of Ottawa
Fundersnot available
KeywordsPaceInformation and Communications TechnologyContext (archaeology)Agile software developmentKnowledge managementCorporate governancePublic relationsComputer scienceBusinessPolitical science

Abstract

fetched live from OpenAlex

The Internet communications technology (ICT) worker of tomorrow will need skills that address the problems of today’s enterprises. This article outlines changing attitudes in project management, their relationship to decision making, and the role of the ICT worker. Large enterprises have traditionally relied on plan-driven project management that presents a clear view of how to engineer and design a product for market. In this view, engineering is “building the thing right” and design is “building the right thing.” Plan-driven models succeed or fail on a top-down ability to define what right means in either context. In today’s market, speed is everything, to the point that many see plan-driven project management failing to keep up with the pace. Other approaches, such as the agile approach, are gaining momentum but focus mostly on reducing time to market while relying on user feedback to define what right means. Who then is best suited to know what right means? Executives at the head of corporate hierarchies, or the people who interact with technology as users? The ICT skills needed to address this question will require a balance between speed and judgment. Participatory governance studies can inform ways to increase speed through devolved decision making, and design ethnography can provide ways that devolved decision makers can cultivate good judgment about their users. We outline this theory in practice with three case studies: delivering educational programs for app development, attempting to broaden civic engagement with natural language processing algorithms, and facilitating Indigenous clean energy projects with design thinking practices.

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.

How this classification was reachedexpand

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 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 score0.979
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.052
GPT teacher head0.356
Teacher spread0.304 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes2
Has abstractyes

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