Bibliographic record
Abstract
You can't have a light without a dark to stick it in. – Arlo Guthrie A December 2012 op-ed in the Moscow Times described State Duma Deputy Oleg Mikheyev's proposal to force Russian media to report more good news. Mass media would have to shift the amount of positive information to 70 percent and restrict bad information to the remaining 30 percent. Too much bad information was said to damage the human psyche – indeed, it “weakens their ability to think and lowers their creative powers.” Michael Bohm, opinion editor of the Times , was of course critical of Mikheyev's (preposterous) bill. Among his reasons, Bohm wrote, “Mikheyev has got the cause-effect relationship of negative information all wrong. The media is much less a cause of society's ills than it is a mirror image of those ills.” Media are certainly as much a reflection as they are a driver of public attitudes. For the most part, media do not make us negative – they reflect our negativity. But whether that negativity is an “ill” is another matter. Focusing on negative information may be a perfectly reasonable means for citizens to monitor their environment, and particularly their governments. Ongoing negativity in politics and political communication may be a problem, but it may also be effective and advantageous.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".