Grant Writing for Service-Learning 1 Revised 4/30/2009 COURSE TITLE: GRANT WRITING FOR SERVICE-LEARNING NO OF CREDITS: 2 QUARTER CREDITS WA CLOCK HRS: 20
Bibliographic record
Abstract
COMPLETION DATE: 3 months from your registration date LEARNING ENVIRONMENT: This course requires assignment responses to be posted in a password-secured ONLINE website hosted by The Heritage Institute. COURSE DESCRIPTION: Academic service-learning has been implemented across the country in an effort to improve student learning and social behavior skills such as civic engagement and participation. Service-learning programs expand teaching and learning beyond the classroom activities by relying on more practical application of the learning that occurs, while impacting authentic issues within the community. There is substantial evidence identifying the importance of grant writing skills across a variety of disciplines, including the effective implementation of service-learning programs in schools and communities. In this course, participants will become proficient in how to: 1) locate an appropriate community issue which parallels an academic framework, 2) determine both the direct and in-kind needs which underpin working with the community issue, and 3) learn to identify and understand the grant structures of the three primary venues for securing external funds; the federal government, state governments, and foundations, as well as engage in authentic grant writing experiences individually, in teams and with community partners. The development of professional grant writing skills is of benefit to educators in a wide range of career opportunities. LEARNING OUTCOMES: By the end of the course participants will:
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.725 | 0.634 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".