Bibliographic record
Abstract
The task set out for us in this curated section of the Yearbook is, from the perspective I present here, problematic. We are invited to consider utterances on the boundaries between speech and song, and I cannot help thinking that this is like being asked to consider bodies at the border between the air and Canada. Though the terms “speech” and “song” both have numerous meanings, speech generally refers to something relatively concrete: the use of the human voice to convey linguistic meaning. The term speech is like the term air; it refers to something intangible but still concrete. Song, on the other hand, is like Canada. It is a reification. How do we address the space between something concrete and something imagined? Song's borders lie at a variety of distinct perceived locations. Unlike with speech, we cannot objectively determine the line between song and non-song. Even if no one shares your sense of where the borders of song lie, no one has the authority to claim you are wrong. Others may be correct to deem your judgment as culturally inappropriate in a given context, but not objectively untrue. If I hear all speech as song, you cannot prove me wrong. If you see all running as dance, I have no solid ground to assert that it's not. We can quibble over intention and the importance of shared cultural conceptions, but ultimately there is no objectively verifiable way to confirm an utterance as song.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".