Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it