L’analyse des configurations du comportement industriel comme technique de prévision
Bibliographic record
Abstract
One important tool for forecasting in the field of applied economics has been largely overlooked—the identification and analysis of patterns of industrial behavior. The following paper shows how the method of detecting behavior patterns, and the factors which control them, can be utilized for analyzing the past economic performance of an industry and for predicting its likely performance in the future. This analysis has also revealed that when controlled by the same factors, different industries follow the same behavior pattern. This being so, it becomes possible to group industries together and reclassify them on the basis of their common major business characteristics. Of these, one of the most important is their mode of competition. This reclassification also enables us to know when we can transfer experience between industries (within the same group) and when we cannot do this (between industries in different groups). At the present time, significant changes are occurring in the economic, organizational and technical climates. New economic goals appear necessary for Canada and these are suggested. These changes are altering the importance of the factors controlling industrial behavior. The nature of these changes is considered and applied to the case of the chemical industry in the next decade. The analysis suggests what new factors will control this industry, and the direction and nature of the likely changes in it. Given this, an estimate of the economic changes can be made. Analysis of behavior patterns is, therefore, a powerful and essential tool for estimating the future behavior of industries, and of groups of industries, in the economy.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".