Bibliographic record
Abstract
Incorporated in 1991 in Boulder Creek, California by Doc Childre, the Institute of HeartMath (IHM) is an innovative nonprofit 501 (c) (3) research and education organization which has developed simple, user-friendly tools people can use in the moment to relieve stress and break through into greater levels of personal balance, creativity, intuitive insight and fulfillment.This technology has formed the foundation of training programs conducted across the United States, Canada, Europe and Asia.These have included programs for major corporations, government and the armed forces; innovative learning enhancement programs for school children; specialized seminars for educators, health and human service professionals; programs for individuals with health challenges; gang risk intervention projects; and family retreats.The tools and technologies developed at IHM offer hope for new, effective solutions to the many daunting problems that currently face our society, beginning by restoring balance and maximizing the potential within the individual. IHM's MissionThrough innovative research and public education, we aim to facilitate more balance and health in people's lives by: Researching the effects of positive emotions on physiology, quality of life, and performance. Helping individuals engage their hearts to transform stress and rejuvenate their health. Providing prevention and intervention strategies for improved emotional health, decisionmaking, learning skills, and violence reduction in communities, families, and schools.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".