Bibliographic record
Abstract
When I first began this book, I naively thought that I had begun an entirely new journey, until one day I pulled out a very short paper I had written in 1974 on organizational behaviour as part of my studies at the Master's level.There were remarkable similarities between that paper and the thoughts that led to this book.Just as my learning about ecological system function, structure, and processes has taught me that systems are sometimes chaotic, random, stable, and discontinuous, there is, at the same time, some sort of remarkable continuity at play that transcends the individual.Thus, I have learned that my work is sometimes chaotic, random, and yet has an emergent integrity that is due, in large part, to my family and friends and the people with whom I have had the privilege of working.I would like to first thank my mother, Catherine, from whom I inherited a strength of mind that has allowed me to continue on in spite of some terrible losses, and my father, who gave me the gift of myself, free from gender constraints.Secondly, my sister, Elaine, who has been a constant source of support and wise editing.And lastly, my husband, Bill, and my beloved child, Daniel James, whose love and support gave me the courage to persevere.Without doubt, my work has taken time from my family, and many times I put those relationships to the side, thinking that I could always get back to them later.Danny was my master literary wizard, my keeper of integrity.In terms of my professional colleagues, I am honoured to count many people who are at once colleagues, friends, and mentors, and who have shared unstintingly of their intellect, time, and support through this incredible journey of reconciliation.I would first like to thank Stuart Hill, Professor, School of Social Ecology, University of Western Sydney at Hawkesbury, for his collaboration
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".