Information technology and the newsprint demand in Western Europe: a Bayesian approach
Bibliographic record
Abstract
This paper focuses on the impacts of new information technology on newsprint demand in a sample of West European countries (Germany, Italy, Spain, and the United Kingdom). It is hypothesized that information technology, through the ready and free availability of news content on the Internet, could induce a structural shift in the newsprint consumption pattern in these markets. Econometric analyses based on historical data for the four countries mentioned above do not yet support this hypothesis. Based on evidence from the United States, where Internet penetration is higher, and several recently published market studies, there is, however, reason to expect stagnating newsprint consumption in Western Europe. By using Bayesian demand models, we try to incorporate prior information from these market studies in the econometric analysis. A classical demand model, based solely on historical data from 1971 to 1999, is estimated for comparison with the Bayesian models. Predictions for newsprint consumption based on the Bayesian approach show lower future consumption levels than those predicted by the classical models, which are commonly used in forest product demand studies. We conclude that Bayesian models carry the potential to improve the quality of forest products demand analyses when a structural break can be expected and sufficient information on its dynamics is available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".