Bibliographic record
Abstract
On a warm Washington, DC evening in mid-May 1979, a reception got underway in the speakers’ dining room in the Capitol building. Cohosted by Paul Findley, a Republican representative from Illinois, and Jim Wright, a Texas Democrat and the House Majority leader, and chaired by Tip O’Neill, the House speaker from Massachusetts, the reception celebrated Clarence Streit, described as the “founder of the Atlantic Union movement and author of ‘Union Now.’” Successive speakers affectionately recalled Streit’s four decades of dedication, in Findley’s words, to the cause of “greater unity among the nations which prize individual liberty.” Wright bestowed on the guest of honor an award named after Estes Kefauver, the deceased senator from Tennessee, former vice-presidential candidate, and Streit’s political ally during the 1950s. Other tokens of admiration included a card signed by almost 200 well-wishers, as well as a book of “congratulatory messages” with entries from former presidents Nixon and Ford, among other political luminaries. Accompanied by Jeanne, his wife and collaborator of over five decades, Streit characteristically urged the attendees to continue the couple’s work. His appeal, though, did little to dent the pervasive sense of nostalgia, of bygone times, and vanishing possibilities. Afterward, Streit quickly faded from view, his death in 1986 at the age of ninety marked by perfunctory obituaries. 1 Today, he is all but forgotten.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.599 | 0.436 |
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".