Bibliographic record
Abstract
Language is widely held to underpin cumulative technology and social institutions. We argue that central to this power of language is one under-acknowledged feature: namely, the reflexivity of language. Language can be used to refer to itself. We first define reflexivity in language, and explicate some of the aspects of language that are made possible by it, including names, reported speech, paraphrase, tense, and pronouns. We then argue that the reflexive property of language has had at least three revolutionary consequences for our species: first, reflexivity enables quoted speech, crucial for reach and reputation management; second, reflexivity enables the building of texts, such as the narratives that build common sense-making as well as legal texts that create social realities; third, reflexivity of language enables social accountability, which is indispensable for the creation of social realities—anything grounded in rights and duties, from ownership to political authority. In the final section, we discuss an apparent paradox arising from the claim that metalanguage is a prerequisite for language, and we speculate that practices of repair in interaction (e.g., saying “Huh?” when one person hasn’t understood what another is doing) may constitute a path by which metalanguage precedes language.
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 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".