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Record W2830405265 · doi:10.18666/jnel-2018-v8-i3-8345

Student-Centered Case Studies in a Nonprofit Leadership and Management Course

2018· article· en· W2830405265 on OpenAlexaff
Peter R. Elson

Bibliographic record

VenueJournal of Nonprofit Education and Leadership · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsScheme (mathematics)Class (philosophy)Bridge (graph theory)Online courseService-learningValue (mathematics)Service (business)PedagogyPsychologyMedical educationPublic relationsSociologyMathematics educationPolitical scienceComputer scienceBusinessMedicineMarketing

Abstract

fetched live from OpenAlex

The value of providing nonprofit leadership and management students with a practical and hands-on experience is well established. A student-centered case study scheme for an in-class and an online course has been developed as an alternative to intense service-learning activities or prescribed volunteering. Students select a nonprofit of their choice for instructor approval and subsequently periodically interview or other-wise investigate the nonprofit throughout the term. Focused assignments bridge theory and practice, culminating in the completion of a major consolidated case study paper. A consolidated case study paper combined with real-time exposure to a community or-ganization provides an opportunity for students to look over the course materials and assignments and develop a strong analysis of the host nonprofit using theories, inter-views, readings, and case study documents. A student-centered investigative-focused case study scheme appears to be a viable and practical way for students, instructors, and nonprofit administrators to mutually benefit.Subscribe to JNEL

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)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0030.002
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.338
GPT teacher head0.449
Teacher spread0.111 · 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 designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

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