Still Bearing the Mark of Cain? Ethics and Inequality Measurement
Bibliographic record
Abstract
In this paper, we build on our previous work thatapplied Amartya Sen and Martha Nussbaum’sAristotelian grounded theory of capabilities tothe workplace in order to propose a new way ofconceptualising workplace equality (Gagnon &Cornelius 2000). Our attention is turned inparticular to Sen’s assertion that equality canonly be understood by recognising the essentialdiversity of human beings.Capabilities theory calls for equal freedoms toachieve and to function. From a capabilitiesperspective, examination of equality action incommunities can only be meaningfully donethrough the use of a richer information base thanis often employed. Such ‘information broadening’is necessary because human diversity results ina range of sources of inequality that specificindividuals and groups may be prone to. Oneimplication is that a review and revision of howworkplace inequality is formally evaluated andthus measured is necessary: this is what we beginto explore in this article. Additionally, we attemptto develop a tentative outline of what might be the‘design elements’ for evaluation and measurementof explicitly ethically grounded approaches toaddressing workplace inequality.
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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 | 0.029 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".