Bibliographic record
Abstract
Students Often Complain About Being Treated Unfairly In School. Some Argue That They Deserve Another Grade. Others Feel That They Have Been Punished Unfairly While Other Pupils Who Behaved More Inappropriately Were Reproved Less. If Students Are Asked To Describe A Good Teacher, Fairness Is Usually One Of The Top Three Characteristics (See Hofer, Pekrun, & Zielinski, 1986). Furthermore, Gage And Berliner (1996) Claim That Unfair Grades Significantly Decrease Students' Achievements. Thus, For Those Who Deal With Everyday Classroom Problems, Dealing With Unfairness And The Consequences Of Unfairness Is A Central Issue. Nevertheless, There Are Few Examples Of Justice Psychology Being Applied In The School Context. This Is A Great Pity. Justice Psychology And, In Particular, Justice Motive Theory Could Significantly Enhance Our Knowledge Of Justice Concerns In School. Melvin Lerner Was The First To Describe The Justice Motive Theory. He Proposed (1965, 1970; Lerner & Simmons, 1966) That People Have The Need To Believe In A Just World In Which All People, Including Themselves, Get What They Deserve And Deserve What They Get. This Belief In A Just World (Bjw) Provides Individuals With The Confidence That They Will Be Treated Fairly By Others And That They Will Not Become Victims Of Unforeseeable Misfortune. Additionally, It Provides A Conceptual Framework That Helps To Interpret The Events Of One'S Personal Life In A Meaningful Way. And This Confidence, Security, And Meaning Serve Important Adaptive Functions.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".