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Record W2895810216 · doi:10.1111/basr.12144

University Mission Statements and Sustainability Performance

2018· article· en· W2895810216 on OpenAlexaff
Yvette P. Lopez, William Martin

Bibliographic record

VenueBusiness and Society Review · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Strategy and Culture
Canadian institutionsWorkplace Health, Safety and Compensation Commission
FundersYoungstown State University
KeywordsSustainabilityMission statementHigher educationEndowmentPublic relationsStatement (logic)Public universitySample (material)BusinessSustainability organizationsPublic institutionPolitical scienceAccountingMarketingPublic administration

Abstract

fetched live from OpenAlex

ABSTRACT This paper examines the relationship between university mission statements and sustainability practices by institutions of higher education. We examine mission statement constructs and the degree to which higher educational institutions meet specific sustainability criteria in line with the College Sustainability Report Card. Our sample consists of 347 universities from the Sustainable Endowment Institute's (2011) Green Report Card. Previous research suggests that mission statements are essential for superior organizational performance outcomes. We examine the relationship between university mission statement content and sustainability practices. Findings indicate that the greater the number of specific terms used in the university mission statements, the higher the statistical likelihood that those universities had higher sustainability ratings. Findings also indicate that private institutions and nonreligious‐affiliated institutions are more likely to include sustainability constructs in their mission statements than colleges and universities with religious affiliation and public institutions. Several propositions to guide future research are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.244
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations43
Published2018
Admission routes1
Has abstractyes

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