Bibliographic record
Abstract
This Motivational Gifts Survey (MGS) is designed as a seven-scale instrument that measures motivational gifts in order to provide profiles that are useful in person-job fit analysis. The seven factors of the instrument include (a) encouraging, (b) mercy, (c) serving, (d) teaching, (e) perceiving, (f) giving, and (g) ruling. The MGS is the first statistically validated gifts survey of its kind. Organizational leaders can use the results of this survey to better place employees and volunteers in ideal job settings that most fully use a person’s gifts. In addition, the results of this survey can help individuals understand their motivational gifts and how to best use those gifts, which could contribute to a sense of personal effectiveness and satisfaction. Bryant (1991), Bugbee, Cousins and Hybels (1994), Flynn (1974), Fortune and Fortune (1987), as well as Gothard (1986) suggested that motivational gifts are indicators of life purpose, thus valuable to the study of job satisfaction and performance in organizations. It has been proven that there is a relationship between a lack of motivation and an increase in apathy with regard to burnout (Maslach & Jackson, 1984). In support of the relationship between motivational gifts and burnout, Bryant (1991) concluded that people, when using their motivational gifts, may wear out, but they do not burn out.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".