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Record W7060819678

Oral History Interview: Michael Zaleski (1172)

2013· other· en· W7060819678 on OpenAlexaboutno aff

Bibliographic record

VenueMinds at UW (University of Wisconsin) · 2013
Typeother
Languageen
FieldPhysics and Astronomy
TopicGyrotron and Vacuum Electronics Research
Canadian institutionsnot available
Fundersnot available
KeywordsOral historySisterMindsetInclusion (mineral)George (robot)Vietnam WarLegal history
DOInot available

Abstract

fetched live from OpenAlex

In his July 2011 interview with Troy Reeves & Mike Lawler, Michael Zaleski detailed his thoughts and memories as an undergraduate & law student on the UW-Madison campus and as a lawyer for the Dane County District Attorney and Wisconsin Attorney General offices. Zaleski spoke of the time period between 1963 and the 1990s and on the following topics: UW-Madison, Sterling Hall Bombing, Dane County District Attorney, Wisconsin Attorney General, the books, Rads, and the Vietnam Era on UW-Madison’s campus. Since he was a lead attorney on the case, he talked in depth about the Karl Armstrong pre-trial investigation and sentence hearing. This interview was conducted for inclusion into the UW-Madison Oral History Program, specifically for the collaboration with the Wisconsin Story Project on the Sterling Hall Bombing.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.171
Threshold uncertainty score0.571

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1710.042

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 head0.025
GPT teacher head0.223
Teacher spread0.198 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2013
Admission routes1
Has abstractyes

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