Bibliographic record
Abstract
limits to population size, species diversity, ecological theory, evolutionary games, evolutionary persistence, value of laboratory experiments in ecology, hydra, coelenterates, Daphnia, cladocerans, scientific dogma. PREFACE As we approach our last quarter of a century, some of us who might by some extension of the word be called writers may be swept away by a desire to summarize. This is my shot at writing a summary. By what standard am I a writer? When I was a little boy in the East Bronx, people would point with great respect to rather ragged men, often with flapping overcoats and scarred shoes and usually with cracked plastic briefcases, and say Ehr ist epes a schreiber! (He is really a writer!) [I don’t quite meet the physical picture. My shoes and briefcase are real leather.] I decided a half-century later that one book did not a schreiber make – everyone has one book in them. Two books is getting closer. Three books defined writer status. Four books or one bestseller was the mark of a hack or a sell-out! I have written three books, two of which are in print. The one that is out of print contributed to my academic advancement nicely. None were bestsellers. This paper consists of reports of events, people, and conversations that I feel taught me important things. I will not attempt to organize them into a formal autobiography. Autobiographies by persons whose works and thoughts are famous can be fascinating, but usually are not. Run-of-themill septagenarians have lived through exciting decades without doing or learning much of general interest. Knowledge does not necessarily provide wisdom, nor does it enforce correct beliefs. Wisdom gained from experience depends as strongly on the person as on the experience itself.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.705 | 0.599 |
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".