Professional fulfillment and satisfaction of US and Canadian adult education and human resource development faculty
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".