Bibliographic record
Abstract
nnovative new products offer agri-food producers and processors an opportunity to differentiate their output from the commodities prevalent in the sector. Unfortunately, many new products do not live up to initial expectations and are eventually abandoned. However, even with products that are doomed to fail, there is occasionally a period of time after their introduction when strong, but fallacious, indicators of success appear. Sometimes the initial appearance of success is so strong that new entrants rush in, increasing demand and prices for production capacity, thereby strengthening the illusion of industry success. Eventually, supply catches up to the unsupported demand and the bubble bursts. The fall is often dramatic and painful. This paper provides a retrospective examination of the economic factors surrounding one example of such a situation, the Ontario emu bubble between 1993 and 1996.1 Implications and Conclusions nnovation, new products and new industries are priorities for governments today. Successful creation of a new industry requires a comprehensive analysis of the markets and the production system needed to meet market requirements. The emu industry provides a worst-case scenario of what can happen when such an approach is not taken. I
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.717 | 0.341 |
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".