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Remembering Erving Polster

2024· article· en· W4402857967 on OpenAlexaboutno aff
Brian O’Neill

Bibliographic record

VenueGestalt Review · 2024
Typearticle
Languageen
FieldPsychology
TopicChild Therapy and Development
Canadian institutionsnot available
Fundersnot available
KeywordsSociology

Abstract

fetched live from OpenAlex

I remember my first meeting with Erving Polster: at my first Association for the Advancement of Gestalt Therapy (AAGT) conference in the mid-1990s during a drinks evening, where I saw him and Miriam Polster talking with people, and so I did not interrupt. I stood back and admired in real life the people (Erv and Miriam) who had taught the man with whom I had trained, Don Diespecker, back in Australia. In those days, everyone in Australia went to the United States to complete training, as our training scene was still developing. It was a time of great change for Australia in Gestalt therapy. Coming to AAGT was a tremendous education for me, and meeting people like Erving and Miriam Polster was like seeing movie stars in Hollywood.It was not until more than a decade later, when I had become president of AAGT and we were having our conference in Vancouver, Canada, that I got to meet and collaborate with Erv in a more intimate way. I was going to give the presidential address and then introduce our keynote speaker, Erving Polster. I remember that, as part of the speech, I used verses from the Australian band, “The Seekers,” formed in Melbourne in 1962. I adopted and adapted some of their verses for AAGT: “We are one, and we are many / And from all the lands of the earth we come / We share a dream, and sing with one voice / I am, you are, we are AAGT.”In his opening address, Erv commended the poem, saying that while it might seem a little folksy, it was totally appropriate for our organization. He then launched into a speech about Life Focus Communities, which meant so much to him. It was during this speech that a most memorable event happened. In the middle of speaking, he suddenly stopped and said, “Miriam is with me now.” The audience gasped. There was a beautiful silence and many tearful eyes. Erv then proceeded with his speech. As someone who has studied the Near-Death Experience, this meant a lot to me and obviously meant a lot to Erv.After the session, I caught up with Erv, and we talked, including about Life Focus. We decided to collaborate on a book, as I was also interested in communities. Joined by Malcolm Parlett, who wrote the Foreword, and Erv, who wrote a Commentary, I edited and produced Community, Psychotherapy and Life Focus: A Gestalt Anthology of the History, Theory and Practice of Living in Community (2012), a compilation of essays by some of the top names in the field of Gestalt therapy. In developing that book with Erv, and later when he wrote the introduction to my edited book, Our Search for Meaning: Essays on Spirituality and Gestalt Therapy (2012), I came to realize what a prolific writer Erv was—constantly coming up with new ideas, writing, and rewriting. Clearly, a key figure in Gestalt therapy, and someone I have come to know, love, and appreciate.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0280.018

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.

Opus teacher head0.029
GPT teacher head0.377
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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