Bibliographic record
Abstract
The very rules of our language games contain mechanisms of disregard. Philosophy of language tends to treat speakers as peers with equal discursive authority, but this is rare in real, lived speech situations. This paper explores the mechanisms of discursive inclusion and exclusion governing our speech practices, with a special focus on the role of gender attribution in undermining women’s authority as speakers. Taking seriously the metaphor of language games, we must ask who gets in the game and whose moves can score. To do this, I develop an eclectic analysis of language games using basic inferential role theory and the concept of a semantic index, and develop the distinction between positional authority and expertise authority, which often conflict for members of oppressed groups. Introducing the concepts of master switches and sub-switches that attach to the index and change scorekeeping practices, I argue that women’s gender status conflicts with our status as authoritative speakers because sex marking in semantics functions as a master switch—“the F-switch”—on the semantic index, which, once thrown, changes the very game. An advantage of using inferentialism for understanding disregard of women’s discursive authority is that it locates the problem in the sanctioned moves, in the deontic structure of norms and practices of scorekeeping, and not primarily in the individual intentions of particular people.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".