Dr. Devinder Singh Sekhon – An Eminent Sikh Scholar devoted to the Sikh Cause
Bibliographic record
Abstract
Dr. Devinder Singh Sekhon served as a Chemistry/Educational Administration professor at various colleges/Universities in Alberta and British Columbia, Canada. Presently, living in Windsor, Canada, he is actively contributing to the fields of Science, Religion, and Literature. Despite being a noted chemist and educationist, he is interested in sharing his insights about religion and science. Due to this keen dedication to sharing his understanding of Sikhi doctrines with all, he authored seven books on various aspects of the Sikh way of life. In addition, his about three dozen stories and two dozen articles have been published in various newspapers and magazines in India, Canada and the USA. His writings, laced with his rationalistic approach and logical outlook, have been well appreciated by his readers. Presently, he is entirely devoted to preparing an English translation of Sri Guru Granth Sahib, the sacred scripture of the Sikhs. In one of my recent meetings with him, I had a chance to learn more about his contributions and insights into Sikhism. A brief write-up of the interview is being shared for the benefit of the readers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".