Freedom to Read vs. Obligation to Protect: New Technologies and Twenty-First-Century Policies
Bibliographic record
Abstract
The explosion of new information technologies and their varied capacity to display and disseminate news, rumours, data, reports, confidential memoranda, and so on have raised anew the issue of freedom to read. This essay is an attempt to compare and contrast the period following World War II with a present-day environment of skirmishes, insurgencies, and small wars—and how a democratic society must cope with conflicting aims: protecting the social order and permitting the dangerous message. The essay reviews a variety of national interests, global constraints, and personal values in order to take account of a new situation that requires urgent attention. It concludes by noting that, on overview, the tensions between personal freedom and political order are of long standing. The capacity of the new information technologies to deliver messages in an accelerated time span is distinctive. The stress between freedom and order is greatest in the absence of a social consensus; it is sharply reduced when such a broad national consensus exists. Since the long span from 1950 to 2010 is marked by the breakdown of a cultural consensus, it is plain that the struggle of competing aims of freedom to read and retention of systemic cohesion is on the global agenda.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.059 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".