Bibliographic record
Abstract
How do we learn the link between speech and money? What is the process of formation that legitimates the logic whereby speech is equivalent to money? What are the experiences, events, and subjectivities that render the connection between currency and speaking/listening intuitive? As educators and researchers, what do we do and say to shore up this connection, and—as we may be inclined—are there things we can do or say to loosen it? Educational discourse theory, specifically examining classroom discussion, is one of the most prominent arenas of educational research where the equivalence between money and speech is active. While it may seem tautological to say that “discussion is an exchange of ideas,” it is not trivial to do so. Definitions of discussion in both reference and academic texts use the word “exchange” for discussion like Kant would use the word “unmarried” to define a bachelor. Yet the exchange case carries connotations and denotations that the bachelor case does not. There is more to say about it. Take this one small case—whether discussion is an exchange of ideas—as a part of the more general inquiry about education and money/speech. Discussions happen throughout classrooms and other educational contexts in society, and the phrase most likely passes person-to-person in such a way as to make it obvious that what is happening in the discussion is an exchange, perhaps making it equally obvious that money and speech are equivalent. In other words, the claim in this article is that continually referring to discussion as an exchange of ideas teaches that speech and money are equivalent, a proposition that has serious political consequences. It is not obviously the case that discussion is an exchange of ideas, as I will show, and demonstrating this with a careful philosophical comb can go some distance towards showing (and therefore teaching) that money is not equivalent to speech.
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 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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.087 |
| Scholarly communication | 0.025 | 0.041 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".