Chapter 04: Golden Record Question and the Earthling Project
Bibliographic record
Abstract
Music: MEDLEY Somewhere Over the Rainbow IZ Source: YouTube https://www.youtube.com/watch?v=V1bFr2SWP1I Wonderful World Louis Armstrong Source: YouTube https://www.youtube.com/watch?v=rBrd_3VMC3c A Love Supreme John Coltrane; Producer: Bob Thiele Producer: George Douglas Associated Performer, Piano: McCoy Tyner Associated Performer, Upright Bass: Jimmy Garrison Associated Performer, Drums: Elvin Jones Composer Lyricist: John Coltrane Source: YouTube https://www.youtube.com/watch?v=TMvbUKqWYEs Ketjak The Ramayan Monkey Chant Source: YouTube https://www.youtube.com/watch?v=R6EFGuXEowk Tunga Mamadou Diabete; Fuseini Kouyate · Ira Coleman · Mamadou Diabate Source: YouTube https://www.youtube.com/watch?v=BDnMf-9QIRI Well You Needn’t Thelonious Monk Thelonious Monk, Coleman Hawkins, Art Blakey, Gigi Gryce Source: YouTube https://www.youtube.com/watch?v=SDbsXep1638 I Have a Dream Martin Luther King Source: YouTube https://www.youtube.com/watch?v=vP4iY1TtS3s END MEDLEY Lullaby Felipe Perez Santiago, The Earthling Project Source: Available on March 24th Painting, video and images: Palenke Arts, Wikimedia Commons, Bob Danziger, Claudia Melendez Salinas, California Rodeo Salinas, Alliance for California Traditional Arts, Earthling Project, Iwanaga Family, Ed Weston, Tanumura and Antle, The Californian, Monterey Herald, Bernie Wire, California State University Monterey Bay, David Ligare
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.206 | 0.081 |
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".