TECHNICAL-ECONOMICAL COMPETITIVENESS OF FOOD INDUSTRY
Bibliographic record
Abstract
In the literature, different definitions of competitiveness exist. The EU Commission (2003) uses as a definition of competitiveness: the ability of an economy to provide its population with high and rising standards of living and a high level of employment for all those willing to work, on a sustainable basis‖. Another definition which is more focused on the manufacturing (Lall, 2001) sectors states: competitiveness in industrial activities means developing relative efficiency along with sustainable growth. According to Canada‘s Agri-Food competitiveness Task Force competitiveness is defined as: the sustained ability to profitably gain and maintain market share (Martin, Westgren v Fischer and Schornberg, 2007). At the firm level, the view of competitiveness can be given as (Buckley, et al., 1988): A firm is competitive if it can produce products and services of superior quality and lower costs than its domestic and international competitors. This paper presents a new approach of technical-economical competitiveness for food industry, and a new type of competitive management of them, so that their technical-economical performance be maximized.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".