Bibliographic record
Abstract
Hear from Carol the benefits of choosing individual thought and actions, stepping away from the need to conform. Dr. Carol Parker Walsh is a career and business coach that helps women who’ve climbed the wrong ladder of success find their life’s work. Carol has coached entrepreneurs, executives, leaders, and professionals including a Grammy Award winner and Paralympic Gold Medalist. With almost 30 years experience as an attorney, management consultant, executive, professor, and dean, Carol runs an award-winning consulting practice and has won national awards for her skills in motivating and inspiring business growth, personal development and leadership. A two-time Amazon #1 best selling author, international speaker, and global thought leader, Carol is the Editor-in-Chief of the AICI Global Magazine and has a column in the Vancouver Business Journal. She appears monthly on ABC affiliate KATU’s AM Northwest Morning Show, as well as been seen in the Huffington Post, Thrive Global, PopSugar, WhoWhatWear and on CBS, NBC and FOX. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.118 | 0.055 |
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".