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Record W2969249385 · doi:10.5430/ijhe.v8n5p176

Understanding Alumni Relations Programs in Community Colleges

2019· article· en· W2969249385 on OpenAlexvenueno aff
Everrett A. Smith, G. David Gearhart, Michael T. Miller

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumInstitutionPublic relationsHigher educationCommunity collegeValue (mathematics)Medical educationPolitical scienceSociologyPedagogyPsychologyMedicineSocial science

Abstract

fetched live from OpenAlex

The purpose for conducting the study was to explore and describe the use of alumni societies and associations in community colleges, particularly focusing on the intended outcome of their implementation. To collect data for the study, a research-team developed survey instrument was distributed electronically to 250 community college advancement senior leaders. A total of 106 (42%) usable surveys were returned for use in the study. The results of the data collection and analysis described community college expectations for alumni societies, and that this expectation was primarily focused on fundraising. The societies were also critical, however, in career placement for students, developing career-oriented programs, and reviewing curricula. Survey results also indicated that many community college leaders use alumni societies in creative ways, including assisting in faculty searches, hosting recruitment events, and teaching community education (lifelong learning) courses. Study findings are critical for college leaders who are often faced with difficulties in funding existing or new programing. The respondents to the study illustrated how alumni societies can serve as critical catalysts for improving existing programs and expanding the reach and value of the institution. Findings also suggest that alumni societies are perceived to be strong gateways to developing philanthropic support for institutions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.148
GPT teacher head0.450
Teacher spread0.302 · 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 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

Citations7
Published2019
Admission routes1
Has abstractyes

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