Bibliographic record
Abstract
Gender is often viewed as static binary state for people to embody, based on the sex they were assigned at birth. However, cultural studies increasingly understand gender as neither binary nor static, a view supported both in psychology and sociology. On this view, gender is negotiated between individuals, and highly dependent on context. Specifically, individuals are thought to be offered culturally gendered social scripts that allow them and their interlocutors the ability to predict future actions, and to understand the scene being set, establishing roles and expectations. We propose to understand scripts in the framework of enactive-ecological predictivism, which integrates aspects of ecological enactivism, notably the importance of dynamical sensorimotor interaction with an environment conceived as a field of affordances, and predictive processing, which views the brain as a predictive engine that builds its probabilistic models in an effort to reduce prediction error. Under this view, script-based negotiation can be linked to the enactive neuroscience concept of a cultural niche, as a landscape of cultural affordances. Affordances are possibilities for action that constrain what actions are pre-reflectively felt possible based on biological, experiential and cultural multisensorial cues. With the shifting landscapes of cultural affordances brought about by a number of recent social, technological and epistemic developments, the gender scripts offered to individuals are less culturally rigid, which translates in an increase in the variety of affordance fields each individual can negotiate. This entails that any individual has an increased possibility for gender fluidity, as shown by the increasing number of people currently identifying outside the binary.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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; both teacher heads agree on what is shown here.
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".