Meeting the JCHE Team: A Reconstructed Interview
Bibliographic record
Abstract
This “reconstructed interview-conversation” involves the entire editorial team of the Journal of Contemplative and Holistic Education (JCHE). Together, the team dialogues around the shared visions, aspirations, motivations, and aims we have in creating this new journal platform. With this sharing, the team sends out a welcoming invitation to colleagues from around the world to get to know our new journal and to join our work to support and promote contemplative and holistic education. We at JCHE are committed to the ideal of education as transformative integration of mind-body-heart-spirit, which we ultimately understand as a decolonizing project. Through this interview, various research and life themes have emerged: the centrality of education for wisdom, the life-sustaining importance of contemplative practice, the role of contemplative and holistic education as decolonial project; how JCHE exemplifies and aims at cultivating the wide diversity that exists in intellectual work; the place of contemplative and holistic education within peace education and environmental education; the important contributions that Indigenous knowledge practices make to education, science, medicine, and health care, and to the broader task of cultivating ecological and human flourishing; as well as concerns about contemplative/holistic practices being co-opted by neoliberal and instrumental forces in education.
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 | 0.037 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".