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Record W2597923845 · doi:10.32469/10355/41908

Campus recreation directors' leadership to provide professional assistance to help students obtain professional and graduate assistant positions

2010· dissertation· en· W2597923845 on OpenAlexaboutno aff
Takeshi Fujii

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationQuarter (Canadian coin)DemographicsGraduate studentsPsychologyMedical educationPublic relationsPedagogySociologyPolitical scienceGeographyMedicine

Abstract

fetched live from OpenAlex

Over the years, the Campus Recreation field has become a dynamic and exciting area with a variety of job and career opportunities. This study attempted to examine the type, frequency and perceived importance of assistance Campus Recreation directors provide for students to obtain a professional or graduate assistant position in the Campus Recreation field. This study found career counseling and résumé advice were the most popular type of assistance Campus Recreation directors provide for both graduate assistants and student employees. Frequency varied from every other year to every semester/quarter depending on assistance. Campus Recreation directors perceived all the assistance items ranging from somewhat important to essential. Most of the participants’ demographics did not make a difference in frequency or perceived importance of assistance. Overall perceived importance was placed more on graduate assistants than on student employees although there was no difference in frequency between graduate assistants and student employees. Finally, Campus Recreation directors provided assistance for students at the frequency based on their perceived importance most of the time.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.002

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 head0.057
GPT teacher head0.391
Teacher spread0.333 · 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 designQualitative
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
Published2010
Admission routes1
Has abstractyes

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