Bibliographic record
Abstract
Preface. L.J. Skitka, N.P. Aramovich, B.L. Lytle, E.G. Sargis, Knitting Together an Elephant: An Integrative Approach to Understanding the Psychology of Justice Reasoning. D.R. Bobocel, A. Zdaniuk, Injustice and Identity: How We Respond to Unjust Treatment Depends on How We Perceive Ourselves. M.J. Callan, J.H. Ellard, Beyond Blame and Derogation of Victims: Just World Dynamics in Everyday Life. C.L. Hafer, L. Gosse, Preserving the Belief in a Just World: When and for Whom are Different Strategies Preferred? D.T. Miller, D.A. Effron, S.V. Zak, From Moral Outrage to Social Protest: The Role of Psychological Standing. J.M. Olson, C.L. Hafer, I. Cheung, P. Conway, Deservingness, the Scope of Justice, and Actions Toward Others. D. Gaucher, A.C. Kay, K. Laurin, The Power of the Status Quo: Consequences for Maintaining and Perpetuating Inequality. J.T. Jost, I. Liviatan, J. van der Toorn, A. Ledgerwood, A. Mandisodza, B.A. Nosek, System Justification: How Do We Know It's Motivated? K. van den Bos, Self-Regulation, Homeostasis, and Behavioral Disinhibition in Normative Judgments. J.M. Darley, D.M. Gromet, The Psychology of Punishment: Intuition and Reason, Retribution and Restoration. T.R. Tyler, Legitimacy and Rule Adherence: A Psychological Perspective on the Antecedents and Consequences of Legitimacy. S.C. Wright, D.M. Taylor, Justice in Aboriginal Language Policy and Practices: Fighting Institutional Discrimination and Linguicide. K. Schumann, M. Ross, The Antecedents, Nature and Effectiveness of Political Apologies for Historical Injustices.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".