MétaCan
Menu
Back to cohort
Record W1488714326

IS sustainability research: A trans-disciplinary framework for a ‘grand challenge’

2012· article· en· W1488714326 on OpenAlexaff
Dirk S. Hovorka, Jacqueline Corbett

Bibliographic record

VenueBond University Research Portal (Bond University) · 2012
Typearticle
Languageen
FieldEngineering
TopicGreen IT and Sustainability
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSustainabilityBiosphereGrand ChallengesSustainability scienceSustainability organizationsSocial sustainabilityDisciplineEngineering ethicsStakeholderStakeholder engagementEnvironmental resource managementKnowledge managementBusinessPolitical scienceManagement scienceSociologyComputer scienceEngineeringEcologyPublic relationsSocial scienceEconomicsBiology
DOInot available

Abstract

fetched live from OpenAlex

To address the “grand challenge” of biosphere sustainability, it is imperative that we examine the assumptions and philosophies underlying Information Systems sustainability research and expand research approaches. Despite calls for trans-disciplinary research and recognition that addressing sustainability will require multiple perspectives, a review of the IS sustainability literature finds that few publications incorporate knowledge or methods from outside traditional businesscentric boundaries. Drawing on a diverse range of IS and sustainability literature, we develop a trans-disciplinary framework for IS Sustainability Research (ISSR) based on a view of sustainability that recognizes the environment as a critical stakeholder rather than a collection of resources to be managed and exploited. We identify three broad areas of inquiry and representative research questions which address the connections between human activity, the natural capital of the biosphere, and the societal goals of human-environment interactions through which ISSR can contribute to the grand challenge of biosphere sustainability.

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)0.003
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.095
GPT teacher head0.340
Teacher spread0.245 · 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
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

Citations19
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueBond University Research Portal (Bond University)Same topicGreen IT and SustainabilityFrench-language works237,207