A Reflective Conversation With Barbara Clark
Bibliographic record
Abstract
Dr Barbara Clark is a Professor Emeritus in the Charter College of Education at California State University, Los Angeles. Dr Clark is the author of the widely used text, ‘Growing Up Gifted’, now in its sixth edition (2002), published by Merrill/Prentice-Hall and ‘Optimizing Learning’ published by the same company in 1986. In addition, she has published many articles in a variety journals, serves as editor of ‘World Gifted’, and is a review editor for several journals including ‘The Gifted Education Communicator’, and ‘Gifted and Talented International’. Dr Clark is the Immediate Past President of the World Council for Gifted and Talented Children, a Past President of the National Association for Gifted Children, and is on the board of Directors and a Past President of the California Association for the Gifted. She was named California State University, Los Angeles Outstanding Professor of 1978–1979 and nominated for California State Universities and College Trustees Award for Outstanding Professor in 1980-1981 and 1984–1985. Dr Clark received the World Council International Distinguished Service award in 2003. Dr Clark has presented major addresses and workshops throughout the United States, Australia, Austria, Canada, England, Mexico, South Africa, the Netherlands, China, Kyrgyzstan, Taiwan, Hong Kong, Spain, Turkey, and Thailand. Her current interests are in the improvement of gifted education, the further development of Integrative Education, a model for optimizing learning that is based on brain/mind research, and research into intuitive intelligence.
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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.038 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".