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Record W2038296180 · doi:10.1068/c11164r

The Gift That Keeps on Giving: Land-Grant Universities and Regional Prosperity

2014· article· en· W2038296180 on OpenAlexaff
Elizabeth A. Mack, Kevin Stolarick

Bibliographic record

VenueEnvironment and Planning C Government and Policy · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLand grantProsperityGovernment (linguistics)Political scienceHigher educationExploratory analysisThe artsInclusion (mineral)AgriculturePublic administrationLand useEconomic growthSociologyGeographySocial scienceEconomicsLawEngineering

Abstract

fetched live from OpenAlex

Land-grant universities are distinctly American institutions of higher education in two respects. First, the establishment of a land-grant university was an independent act by the US federal government that endowed specific counties across the country with a university. Second, their mission of inclusion, with an emphasis on the agricultural and mechanic arts, was designed to educate the industrial class for professional life. Despite these institutions' unique founding and mission, however, land-grant universities have received little specific attention in the broader literature on university impacts. Given the comparatively little attention devoted to these institutions, the goal of this study is to use a descriptive and exploratory quasi-experimental analysis to evaluate the potential impacts of land-grant institutions on their local communities. The results of this analysis suggest that land-grant universities do impact their local communities, but that these impacts did not begin to appear until approximately sixty years after their initial founding.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.704

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 studies0.0010.000
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 head0.017
GPT teacher head0.259
Teacher spread0.241 · 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 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

Citations14
Published2014
Admission routes1
Has abstractyes

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