Erving Polster in the (International) Association for the Advancement of Gestalt Therapy
Bibliographic record
Abstract
In 1997, I was the president of the Association for the Advancement of Gestalt Therapy (AAGT), later renamed to International Association for the Advancement of Gestalt Therapy (IAAGT). I was a nervous wreck as I waited for the end of a conference dinner and reception to give my presidential speech. What could I possibly say to this crowd consisting of my mentors and my gurus? I swallowed hard and decided to look my audience in the eye. I remember looking at Joseph Zinker, Elaine Kepner, Richard Kitzler, Robert Resnick, Gary Yontef, and many, many others whom I strove to emulate. These were the famous Gestalt therapists I had hoped to rub elbows with when I joined the conferences initially sponsored by the Gestalt Journal1 and later IAAGT. I had arrived, and in that very moment I found myself inarticulate.I continued to scan the room, and I came to Miriam’s Polster’s face. She looked loving and kind. Next to her was Erving Polster. He looked formidable to me. Big bushy eyebrows, and a gaze that really saw me. He was inviting, warm, and formidable all at the same time. In this very moment, I recognized it was not Erv that I was afraid of; it was me. I blurted out to the audience, “I don’t know why I’m looking at you; I know I’ll find what I need to say in me.” Erv smiled, and I settled into myself. What I am perfectly clear about is that Erving Polster was a lovely and brilliant man, dedicated to his craft and contacts. I was lucky to get to know Erv.Erving Polster was a rock and a founding member of AAGT. Those early days were highly contentious, and community meetings often followed a process of wrestling the microphone from one angry person to another. Our individuation from the Gestalt Journal was both critical and extremely difficult. Through it all, there was lots of polarization. If anyone in the organization stood for middle ground, it was Erving Polster. Both he and Miriam Polster were honored with AAGT’s Lifetime Achievement Award at the 1999 AAGT Conference in New York. Erv received a second, unprecedented lifetime achievement award in Vancouver at the 2006 AAGT Conference, where he was also the keynote speaker. Erving Polster is the only one to have received two lifetime achievement awards. He deserved them both.Needless to say, his warmth and brilliance will be missed, and I for one will be grateful for the balance, reason, and passion he brought to IAAGT.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 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".