Bibliographic record
Abstract
By their very nature, democracies around the world affirm belief in intellectual freedom of thought, speech and written word. In every such society, however, a counterforce exists that would limit such freedom and impose restrictions and sanctions against material viewed by some as anti-moral or anti-social. Free speech and censorship are opponents on a battlefield where each side to the conflict feels a righteous and indignant claim on behalf on its own cause. This paper presents a psychological perspective which tries to understand censorship - where it comes from, who "has it" and why, and how it functions in all human beings to keep them psychologically safe and sane. Based on personality theory, this paper explores the psychological indications for censorship as a human dynamic and its bases in family ideology, social group norms, demographic factors and individual personality development. This paper does not consider the legal, moral or politcal aspects of censorship, nor does it recount the may blatant subtle censorship events and conditions in countries around the world. Rather, it considers the individual and group conditions underlying personality development that are likely to result in the individual's inclination to assume legal, moreal and political censorship activities.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".