Design of a differential cantilever-based sensor : surface stress of self-assembled alkanethiols on gold-coated cantilevers
Bibliographic record
Abstract
This exploratory study examined the predictive ability of perceived work environment characteristics on employees' level of motivation to learn and growth need strength. It looked at motivation to learn within the context of two types of training: formal training and on-the-job training. It also examined the existence of group differences in motivation and in perceptions of the work environment. The sample was 117 middle management staff at a Canadian research university, varying in age, level of education, job classification, work unit, and job and institutional tenure. Data was collected using a questionnaire consisting of scales from the management and educational literature. Using multiple regression analysis and MANOVAs, workplace environmental characteristics were found to be predictors of employee motivation. The best predictor of motivation to learn was a composite measure of incentives, while the best predictor of growth need strength was a composite measure of lack of independence and freedom of choice. No group differences in motivational characteristics were found, however, there were differences in perceptions of the work environment.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".