If It Is To Be, It Is Up To Me: The Power of You
Bibliographic record
Abstract
Change. We want it, but what are we willing to do for it? Kimberely Dixon's concept “If it is to be it is up to me” puts you in the driver seat, accelerates your growth and can create tangibles in any area of your life. Kimberely Dixon has spent over 25 years in the Portland/Vancouver area with a flourishing family of ten children and 14 grandchildren. She acquired her post-secondary education in Business Management and Organizational Leadership while raising her family. Kimberely looks forward to finishing up final credits for her second Master's degree in Adult Postsecondary Education. Her areas of expertise are in advocacy, training/facilitation, organizational leadership and behavior, starting over and being a voice to the voiceless. Her natural born advocacy has found a place after a significant loss of one of her children to gun violence. She currently serves as a member of the Crisis Response Team, Board of Trustees Trillium Family services and has recently completed serving as Co-Chair for 3 to Phd, a collaborative partnership between Faubion PK-8 and Concordia University, Kaiser Permamente, Trillium and Basics Foods to create a safer, healthier and more educated community.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".