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Record W4385871140 · doi:10.59962/9780774855419-006

Sustainability, Social Capital, and the Canadian ICT Sector

2007· book-chapter· en· W4385871140 on OpenAlexaboutno aff
David Wheeler, Kelly Thomson, Michael A. Perkin

Bibliographic record

VenueUniversity of British Columbia Press eBooks · 2007
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologySocial capitalSustainabilityBusinessEconomic growthPolitical scienceEconomicsSociologySocial scienceEcology

Abstract

fetched live from OpenAlex

In this paper we explore the possibilities of information and communication technology (ICT) companies creating economic, social and ecological value – primarily with reference to two\ntheoretical constructs: sustainability and social capital. We take a global overview and provide commentary on private and public sector applications of digital technologies around the world.\nWe develop a Canadian perspective on sustainability, social capital and the ICT sector and we report on preliminary research with the sector undertaken for the Sustainable Canada project. We observe that at the present time Canadian ICT companies do not see the issue of sustainability as a defining characteristic of their identities or of their business strategies. However, there is a good understanding of the importance of practical commitments to\nenvironmental and social good practice and there is potential for ICT companies based in Canada to enter into strategic partnerships with government and civil society organizations in order to help address a range of social and ecological issues in Canada and internationally. We suggest that pursuit of ‘made in Canada’ initiatives in sustainable development may enhance social capital in the ICT sector which may in turn improve the reputation and competitiveness of Canadian ICT firms.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.022
GPT teacher head0.182
Teacher spread0.161 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2007
Admission routes1
Has abstractyes

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