Imagine: Using Mental Imagery to Reach Your Full Potential
Bibliographic record
Abstract
Did you know that images of the mind shape how we think, feel and behave? The way we frame scenarios has the power to impact our attitude and actions. But what if we could consciously choose to cast things in a positive light? Equipping you with the tools you need to harness the power of mental imagery, ‘Imagine’ will help you achieve your goals and reach your full potential. With Dr Lydia Ievleva’s extensive experience incorporating mental imagery with a wide range of athletes, business and health professionals, her tried and tested advice will allow you to take back control. Featuring case studies and practical tips to set bitesize goals, forging the right mindset has never been so easy. \"You can think of imagery like a screenplayyou can allow life to play you; or you can take a more active role in your destiny’ Dr Lydia Ievleva With over 25 years of experience in practice and teaching, Dr Lydia Ievleva is a psychologist with extensive experience supporting a wide range of clients. Former president of the APS College of Sport and Exercise Psychologists, she received her training in Canada and the U.S. Lydia holds the following qualifications: BA Hons Psychology, Carleton University; MSc Sport Psychology, University of Ottawa; and PhD Counselling Psychology, specialising in health and sport, Florida State University. Her work includes clients seeking health, wellbeing, relationship, performance and professional goals and she has helped countless Olympic and professional athletes, dancers, musicians, artists, writers, corporate clients. Her popular psychology books are now helping millions around the world reach their full potential.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 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".