An Exploration of Fairness: Interdisciplinary Inquires in Law, Science and the Humanities
Bibliographic record
Abstract
An Exploration of Fairness examines a concept that is simultaneously simple and extraordinarily complex – fairness. Simple in that we often intuitively understand situations to be fair or unfair. Complex, in that the notion of fairness is very much grounded in where we are situated, including race, gender, class, economic status, country, and life experience. There is considerable scholarship on the concept of fairness, but this volume is unique in the broad range of research disciplines that have come together to examine in depth what is meant by fairness, how it can be achieved, measured, shared. From its application in law, economics and business to how it can be interpreted in cognitive neuroscience, developmental psychology and kinesiology, it integrates the visual and performing arts as essential features of fairness. Through this interdisciplinary lens, the ethical and normative dimensions of fairness are understood, drawing on its historical and philosophical origins and its role in citizenship and political obligation. Each discipline, each chapter, informs the others, to deepen our understanding of fairness.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.062 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| 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".