What is it About our Story: Does Ergodicity help us Understand Equity from a Neurodiverse Perspective?
Bibliographic record
Abstract
This article explores the dynamics of equity and ergodicity in a psychological lab context including navigating consent (commitments) and transparency (debriefs). The article explores how evolutionary determinants are translated into competitive gameplay in human social interactions and how cooperative gameplay based on cultural stories counteracts harms associated with competition. Other themes that are explored is a love of learning at the center of cooperative storytelling. An Indigenous form of perspective-taking called etuaptmumk or "two-eyed seeing," developed by First Nations Mi'kmaw Elder Albert Marshall, is used as an example of ergodic intervention as a balance to cognitive biases. How are concepts of dignity and respect, as support for equity in needs, and a recognition of community member competencies and contributions, work to nurture a neurodiverse writing community where individuals can openly navigate consent, transparency, consensus, and inclusion? What are both the theoretical and practical implications of using multimodal expression such as writing on a neurodiverse community?
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".