Bibliographic record
Abstract
We are the speaking animals. To take a leaf from Aristotle’s book, the permanently mute or unremittingly taciturn are inferior beasts or superior gods; to be a person is to be gregarious, and to associate in the human fashion is to speak among ourselves. Indeed, creation and discovery of the individual self are conditional on locating this self among other selves; but since these others are nodes in a linguistic network, a society of speakers, acquisition of a language and entry into a community necessarily proceed in tandem. This skeletal delineation of linguistic essentialism will strike some as at best tendentious, at worst grotesquely unscientific, bereft of empirical foundations. No matter: for overwhelmingly many others, the absolute centrality of language is a truism so conspicuous as barely to deserve acknowledgment, before one passes on to the burgeoning questions to which it immediately gives rise: is all thought linguistic? Do the expressive resources of all languages come to the same sum, or do different languages manifest characteristic (dis)advantages? With such linguistic relativism, we are moving from what appears to be strictly theoretical to issues with ethical import; consideration of the possibility that language might encode, enshrine, confirm, or even partially comprise various sociopolitical asymmetries takes us further along the spectrum. Participants in such debates may hotly disagree, while fundamentally agreeing that language is always, inevitably at the core of their dispute. Profound insight, or profound delusion: if the latter, this chapter is a modest foray into the pathology of our exaggerated logocentric proclivities.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".