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Record W2068574976 · doi:10.1080/0260137042000196441

Professional fulfillment and satisfaction of US and Canadian adult education and human resource development faculty

2004· article· en· W2068574976 on OpenAlexaffabout
Shari L. Peterson, Faye Wiesenberg

Bibliographic record

VenueInternational Journal of Lifelong Education · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProfessional developmentAdult educationFaculty developmentPsychologyHigher educationPedagogyResource (disambiguation)SociologyMedical educationPublic relationsPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Abstract This comparative study explored the professional fulfillment and job satisfaction of US and Canadian college and university faculty in the fields of Adult Education and Human Resource Development. In Autumn 2001, we disseminated electronically The Adult Education and Human Resource Development Faculty Survey to a selected sample of Canadian and US faculty from across the continent. Results showed few differences between the US and Canadian faculty in terms of sources of satisfaction and dissatisfaction, suggesting a commonality among members of the profession. However, within Canada, there were some differences between male and female faculty and between the fields. Specific aspects of professional fulfillment, responsible for overall career satisfaction, varied somewhat between Canadian and US faculty. Overall, faculty members were relatively satisfied with their careers and would choose the same careers if they had it to do over again. However, 'change is in the air' in academia, and declining job satisfaction may become an important issue for US and Canadian college and university administrators who will soon face the challenge of replacing a wave of baby‐boom professorial retirements with a predicted shortage of new PhD graduates. This study advances our understanding about the need to improve organizational climates in order to build on an already satisfying profession.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.949

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 communication0.0000.000
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 head0.019
GPT teacher head0.408
Teacher spread0.389 · 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

Citations16
Published2004
Admission routes2
Has abstractyes

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